CN112572281A

CN112572281A - Light intensity adjusting method and device, electronic equipment and storage medium

Info

Publication number: CN112572281A
Application number: CN201910925583.1A
Authority: CN
Inventors: 程光亮; 石建萍
Original assignee: Beijing Sensetime Technology Development Co Ltd
Current assignee: Beijing Sensetime Technology Development Co Ltd
Priority date: 2019-09-27
Filing date: 2019-09-27
Publication date: 2021-03-30
Also published as: WO2021057244A1; JP2022550300A; KR20220062107A

Abstract

The present disclosure relates to a light intensity adjusting method and apparatus, an electronic device, and a storage medium, the method including: determining the distance between a target object in the image and the intelligent driving equipment according to the image acquired by the intelligent driving equipment; and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device according to the distance between the intelligent driving device and the target object. The embodiment of the disclosure can realize automatic adjustment of the light intensity of the illuminating lamp.

Description

Light intensity adjusting method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of driving assistance technologies, and in particular, to a light intensity adjusting method and apparatus, an electronic device, and a storage medium.

Background

In recent years, driving assistance techniques have been developed along with the spread of vehicles. The driving assistance technology may assist safe driving of a vehicle, and more driving assistance functions have been applied to automatic driving of a vehicle, for example, a lane keeping function, an automatic parking function, a brake assistance function, and the like. The auxiliary driving technology can better assist the user to safely drive, and provides convenience for the user.

Disclosure of Invention

The present disclosure provides a light intensity adjusting technical scheme.

According to an aspect of the present disclosure, there is provided a light intensity adjusting method including:

determining the distance between a target object in the image and the intelligent driving equipment according to the image acquired by the intelligent driving equipment;

and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device according to the distance between the intelligent driving device and the target object.

In one possible implementation manner, determining a distance between a target object in an image and a smart driving device according to the image acquired by the smart driving device includes:

determining the position of a target object in the image according to the image;

and determining the distance between the target object and the intelligent driving equipment according to the position of the target object.

In one possible implementation, determining a position of a target object in the image according to the image includes:

determining image coordinates of the target object in the image according to the image;

determining a position of the target object in the image based on image coordinates of the target object in the image.

In one possible implementation, determining a position of the target object in the image based on image coordinates of the target object in the image includes:

converting the image coordinates of the target object into world coordinates under a world coordinate system according to a coordinate transformation relation;

the determining the distance between the intelligent driving device and the target object according to the position of the target object comprises:

and determining the distance between the target object and the intelligent driving device according to the world coordinates of the target object and the world coordinates of the intelligent driving device.

In one possible implementation, the coordinate transformation relationship is determined by:

acquiring an annotation image;

determining the image coordinates of the annotation points in the annotation image;

and determining the coordinate transformation relation between the image coordinate and the world coordinate according to the image coordinate of the labeling point and the world coordinate labeled in advance.

In a possible implementation manner, the determining, according to an image acquired by a smart driving device, a distance between a target object in the image and the smart driving device includes:

determining the number of reference objects between the target object and the intelligent driving device according to the image; wherein the spacing between two adjacent reference objects is known;

and determining the distance between the intelligent driving equipment and the target object according to the number of the reference objects between the target object and the intelligent driving equipment and the distance between the adjacent reference objects.

In one possible implementation manner, the target object is a plurality of objects, and the adjusting of the light-emitting intensity of the illumination lamp of the smart driving device according to the distance between the smart driving device and the target object includes:

and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device according to the minimum distance between the plurality of target objects and the intelligent driving device.

In one possible implementation, adjusting the light-emitting intensity of an illumination lamp of the smart driving device according to the distance between the smart driving device and the target object includes:

determining target luminous intensity corresponding to the distance between the intelligent driving equipment and the target object according to the corresponding relation between the target distance and the target luminous intensity and the distance between the intelligent driving equipment and the target object;

and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device to the target luminous intensity.

In one possible implementation manner, the determining, according to a correspondence between a target distance and a target luminous intensity and a distance between the smart driving device and the target object, the target luminous intensity corresponding to the distance between the smart driving device and the target object includes:

determining a first distance interval to which the distance between the intelligent driving equipment and the target object belongs in a list of corresponding relations between target distances and target luminous intensities;

and determining the target luminous intensity corresponding to the distance between the intelligent driving equipment and the target object according to the target luminous intensity corresponding to the first distance interval in the list.

determining a second distance interval in which the distance between the intelligent driving equipment and the target object is located according to the distance between the intelligent driving equipment and the target object and a function representing the corresponding relation between the target distance and the target luminous intensity; the second distance interval is a distance interval of the function;

and determining the target luminous intensity corresponding to the distance between the intelligent driving equipment and the target object according to the calculation mode of the target distance corresponding to the second distance interval and the target luminous intensity in the function.

In one possible implementation, the method further includes:

when it is determined that the target object does not exist in the image according to the image, adjusting the luminous intensity of an illuminating lamp of the intelligent driving device to a preset luminous intensity, or keeping the luminous intensity of the illuminating lamp of the intelligent driving device unchanged.

According to another aspect of the present disclosure, there is provided a light intensity adjusting apparatus including:

the determining module is used for determining the distance between a target object in the image and the intelligent driving equipment according to the image acquired by the intelligent driving equipment;

and the adjusting module is used for adjusting the luminous intensity of an illuminating lamp of the intelligent driving equipment according to the distance between the intelligent driving equipment and the target object.

In a possible implementation manner, the determining module is configured to determine, according to the image, a position of a target object in the image; and determining the distance between the target object and the intelligent driving equipment according to the position of the target object.

In one possible implementation, when determining the position of the target object in the image according to the image, the determining module is configured to:

determining image coordinates of the target object in the image according to the image; determining a position of the target object in the image based on image coordinates of the target object in the image.

In one possible implementation, the determining module, when determining the position of the target object in the image based on the image coordinates of the target object in the image, is configured to:

the determining module is used for determining the distance between the target object and the intelligent driving device according to the position of the target object, and comprises the following steps: and determining the distance between the target object and the intelligent driving device according to the world coordinates of the target object and the world coordinates of the intelligent driving device.

In one possible implementation, the determining module is further configured to determine the coordinate transformation relationship by:

acquiring an annotation image;

In a possible implementation manner, the determining module is configured to determine, according to the image, the number of reference objects between the target object and the smart driving device; determining the distance between the intelligent driving equipment and the target object according to the number of the reference objects between the target object and the intelligent driving equipment and the distance between the adjacent reference objects; wherein the distance between two adjacent reference objects is known.

In a possible implementation manner, the target objects are multiple, and the adjusting module is configured to adjust the light emitting intensity of an illumination lamp of the smart driving device according to the minimum distance between the multiple target objects and the smart driving device.

In a possible implementation manner, the adjusting module is configured to determine, according to a correspondence between a target distance and a target luminous intensity and a distance between the smart driving device and the target object, a target luminous intensity corresponding to the distance between the smart driving device and the target object; and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device to the target luminous intensity.

In a possible implementation manner, the adjusting module is configured to, when determining a target luminous intensity corresponding to a distance between the smart driving device and the target object according to a correspondence between a target distance and a target luminous intensity and a distance between the smart driving device and the target object, include:

In a possible implementation manner, the determining module is further configured to determine, according to the image, that a target object does not exist in the image;

the adjusting module is further used for adjusting the luminous intensity of the illuminating lamp of the intelligent driving device to a preset luminous intensity, or keeping the luminous intensity of the illuminating lamp of the intelligent driving device unchanged.

According to an aspect of the present disclosure, there is provided an electronic device including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to: the above-described light intensity adjusting method is performed.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described light intensity adjusting method.

In the embodiment of the disclosure, the distance between the target object in the image and the intelligent driving device and the target object can be determined according to the image acquired by the intelligent driving device, and then the luminous intensity of the illuminating lamp of the intelligent driving device is adjusted according to the distance between the intelligent driving device and the target object. Therefore, the distance between the target objects such as pedestrians and vehicles and the intelligent driving equipment can be determined through the collected images, and the luminous intensity of the illuminating lamp of the intelligent driving equipment is automatically adjusted according to the distance, so that convenience is brought to vehicle driving, the driving safety of the vehicle is improved, and traffic accidents are reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flow chart of a light intensity adjusting method according to an embodiment of the present disclosure.

Fig. 2 illustrates a flowchart of an example of determining image coordinates of a target object according to an embodiment of the present disclosure.

FIG. 3 shows a block diagram of an example of target detection using a neural network, in accordance with an embodiment of the present disclosure.

Fig. 4 shows a block diagram of a light intensity adjusting device according to an embodiment of the present disclosure.

Fig. 5 shows a block diagram of an example of an electronic device in accordance with an embodiment of the present disclosure.

FIG. 6 shows a block diagram of an example of an electronic device in accordance with an embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

According to the light intensity adjusting scheme provided by the embodiment of the disclosure, the distance between the target object in the image and the intelligent driving device and the target object can be determined according to the image collected by the intelligent driving device, and then the light emitting intensity of the illuminating lamp of the intelligent driving device can be adjusted according to the distance between the intelligent driving device and the target object. Therefore, the distance between the intelligent driving device and a target object such as a pedestrian or a vehicle can be determined through the image acquired by the intelligent driving device, and then the luminous intensity of the illuminating lamp can be automatically adjusted according to the distance, for example, under the condition that no vehicle or pedestrian exists in the front, the luminous intensity of the illuminating lamp can be adjusted to be in a strong state, under the condition that the vehicle or pedestrian exists in the front, the luminous intensity of the illuminating lamp can be adjusted to be in a weak state, the safe driving of the current vehicle or the front vehicle is facilitated, the driving safety of the vehicle is improved, and the occurrence of traffic accidents is reduced. In addition, the light intensity adjusting scheme provided by the embodiment of the disclosure has the advantages of simple light intensity adjusting mode, flexible and convenient application, and no need of deploying other related devices, such as Bluetooth devices, hotspot devices, infrared devices and the like, for light intensity adjustment, thereby reducing the cost of light intensity adjustment.

In the related art, in an environment with dark light, if the light intensity of a vehicle is strong, for example, in the case of using a high beam, the high beam has a strong luminous intensity, which may cause a great influence on pedestrians or drivers of other vehicles, and easily cause traffic accidents. The light intensity adjusting scheme provided by the embodiment of the disclosure can determine the distance between the intelligent driving device and the target object through the image collected by the intelligent driving device, automatically adjust the luminous intensity of the illuminating lamp according to the distance, improve the safety of the vehicle in the driving process, and bring great convenience for traffic driving.

The light intensity adjusting scheme provided by the embodiment of the present disclosure is explained by the following embodiments.

Fig. 1 shows a flow chart of a light intensity adjusting method according to an embodiment of the present disclosure. The light intensity adjusting method may be performed by a terminal device, a server, or other information processing device, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a smart driving device, a wearable device, or the like. In some possible implementations, the light intensity adjusting method may be implemented by the processor calling computer readable instructions stored in the memory. The following describes the light intensity adjustment scheme of the embodiment of the present disclosure by taking the smart driving device as an execution subject.

As shown in fig. 1, the light intensity adjusting method includes the steps of:

step S11, according to the image collected by the intelligent driving device, determining the distance between the target object in the image and the intelligent driving device.

In the embodiment of the present disclosure, the intelligent driving device may be an automobile, a robot, or the like may run on a road surface, and has a device that an illumination lamp may illuminate under the condition of insufficient light, and the intelligent driving device may be configured with an image capturing device, such as a camera or the like. The intelligent driving device can acquire the image of the current scene in real time and then perform target detection on the acquired image. And determining whether the target object exists in the acquired image or not according to the detection result of the target detection on the image. In the case where it is determined that the target object exists in the image, the distance between the target object and the smart driving device may be determined by the image position of the target object in the image, or the distance between the target object and the smart driving device may be determined by the number of reference objects between the target object and the smart driving device, that is, the number of reference objects between the target object and the smart driving device in the captured image along the image capturing direction.

When the target detection is carried out on the image, the image feature extraction can be carried out on the image by utilizing the pre-trained neural network, so that the image position of the target object in the target image is obtained. Alternatively, the target image may be matched with a pre-stored image template, and it is determined whether there is a projection of the target object in the target image, and if there is a projection of the target object, the image position of the target object in the image may be further determined. Then, from the image position of the target object, the position of the target object in the current scene can be further determined. The position of the target object in the current scene may be an exact position, e.g. may be world coordinates in a world coordinate system, or may be an approximate position. The target object may be a pedestrian, a motor vehicle, a non-motor vehicle, or the like that can pass through the traffic road.

In one possible implementation manner, when it is determined that the target object does not exist in the image according to the acquired image, the light emitting intensity of the illuminating lamp of the intelligent driving device may be adjusted to a preset light emitting intensity, or the light emitting intensity of the illuminating lamp of the intelligent driving device may be kept unchanged.

Here, if the target object is not detected in the captured image, the light emitting intensity of the illumination lamp may be adjusted to a preset light emitting intensity, which may be a stronger light emitting intensity, so that the illumination lamp may be adjusted to a stronger light emitting intensity in the case where there is no target object such as a pedestrian or a vehicle in the current scene, thereby providing a good illumination condition for the intelligent driving apparatus. Alternatively, the luminous intensity of the illuminating lamp can be kept unchanged, so that the intelligent driving device keeps the current illumination condition.

In one possible implementation, the smart driving device may determine a position of a target object in the image according to the acquired image, and then determine a distance between the target object and the smart driving device according to the position of the target object.

In this implementation, in the case that the smart driving device detects that the target object exists in the image, the smart driving device may determine the position of the target object in the world coordinate system according to the image position of the target object in the image. The distance between the intelligent driving device and the target object can be determined according to the position of the target object in the world coordinate system and the current position of the intelligent driving device.

In the case where there are a plurality of target objects, the distance between each target object and the smart driving device may be determined according to the position of each target object in the world coordinate system.

In one example, the image coordinates of the target object in the image may be determined from the image captured by the smart driving device, and then the position of the target object in the image may be determined based on the image coordinates of the target object in the image.

In this example, the image coordinates of the target object in the image may be understood as image coordinates corresponding to the projection of the target object in the target image. By carrying out target detection on the image acquired by the intelligent driving equipment, the image coordinates of the target object in the image can be determined. Then, according to the image coordinates of the target object in the image, the position of the target object in the current scene may be determined, which may be world coordinates in a world coordinate system. The current scene can be a scene of lighting by starting a lighting lamp in a dark environment, and the target detection of the collected target image can be under the condition of starting the lighting lamp. The image coordinates of the target object in the image may be center coordinates or average coordinates of the projection of the target object in the image, or may be image coordinates of any point of the projection of the target object in the image.

In one example, the image coordinates of the target object may be converted into world coordinates in a world coordinate system according to a coordinate transformation relationship, and then the distance between the target object and the smart driving device may be determined according to the world coordinates of the target object and the world coordinates of the smart driving device.

Here, the coordinate transformation relation may be a transformation relation in which image coordinates of a target image captured by the smart driving device are transformed into world coordinates, and by the coordinate transformation relation, image coordinates of a space point of a three-dimensional space projected in the image may be transformed into world coordinates of the space point. The image coordinates of the target object can be converted into world coordinates in a world coordinate system through the coordinate transformation relation, so that the position of the target object can be determined quickly. The intelligent driving device can acquire the world coordinate of the current position detected by the navigation system or the positioning system, so that the distance between the intelligent driving device and the target object can be obtained by using the difference value between the world coordinate of the current position of the intelligent driving device and the determined world coordinate of the target object. Through the method, the accurate distance between the intelligent driving equipment and the target object can be obtained.

In one example, an annotation image can be obtained, then the image coordinate of an annotation point in the annotation image is determined, and then the coordinate transformation relation between the image coordinate and the world coordinate is determined according to the image coordinate of the annotation point and the world coordinate which is annotated in advance.

In this example, the annotation image can be a captured image with annotation information. The annotation information may be world coordinates of spatial points corresponding to the pixel points in the annotated image. According to the image coordinates of the plurality of spatial points and the world coordinates in the annotation information, a coordinate transformation relationship that is transformed from the image coordinates to the world coordinates can be determined, and the coordinate transformation relationship can be represented by a transformation matrix. For example, the coordinate transformation relationship may be determined according to the image coordinates and world coordinates of 4 spatial points in one annotation image. For another example, the coordinate transformation relationship may be determined according to the image coordinate and the world coordinate of a spatial point of each of the 4 annotation images. In this way, the coordinate transformation relation between the image coordinates and the world coordinates in the current scene can be determined quickly and accurately.

In one possible implementation manner, the number of reference objects between the target object and the smart driving device may be determined according to the image acquired by the smart driving device, and then the distance between the smart driving device and the target object may be determined according to the number of reference objects between the target object and the smart driving device and the distance between adjacent reference objects. Here, the spacing between two adjacent reference objects is known.

In this implementation, the smart driving device may identify an image captured by the smart driving device, and determine a target object and a reference object in the image. The reference object may be a landmark object in which location information exists, for example, the reference object may be a street lamp, a trash can, a tree for greening, and the like. The position information of the reference object may be a distance between adjacent reference objects. Then, the number of reference objects in the foreground of the target object in the image and the number of reference objects existing in the foreground of the target object can be counted, and the number of reference objects existing between the target object and the intelligent driving device can be considered as the number of reference objects existing between the target object and the intelligent driving device, so that the distance between the intelligent driving device and the target object can be further determined according to the number of reference objects between the target object and the intelligent driving device and the distance between the adjacent reference objects. For example, assuming that the number of street lamps between the current target object and the smart driving device is determined to be 3 according to the acquired image, if the distance between adjacent street lamps is 10 meters, it may be determined that the distance between the target object and the smart driving device is greater than or equal to 20 meters.

In one example, the position information of the reference object may be world coordinates, and in this case, the distance between the smart driving device and the reference object may be determined first according to a relative positional relationship between the reference object and the target object, for example, the target object is far away from the smart driving device relative to the reference object, so that the distance between the smart driving device and the reference object may be estimated according to the world coordinates of the smart driving device and the world coordinates of the reference object, and the distance between the target object and the smart driving device is greater than the distance between the smart driving device and the reference object.

Here, the smart driving device may determine the target distance between the target object and the smart driving device without configuring other distance measuring devices, for example, without configuring distance measuring devices such as an infrared device and a laser device, and is simple and easy to implement. In addition, in some embodiments, the smart driving device may multiplex the acquired target image, for example, when the target image is used to determine the distance between the target object and the smart driving device, the target image may also be used to determine whether the smart driving device deviates from the lane or other operations.

And step S12, adjusting the luminous intensity of an illuminating lamp of the intelligent driving device according to the distance between the intelligent driving device and the target object.

In the embodiment of the present disclosure, the smart driving apparatus may adjust the light intensity of the illumination lamp according to the distance between the smart driving apparatus and the target object, for example, in a case that the distance between the smart driving apparatus and the target object is greater than a preset threshold, the light intensity of the illumination lamp may be kept unchanged, or the light intensity may be adjusted to a first light intensity, which may be a stronger light intensity, so that a good illumination condition may be provided for the smart driving apparatus. Under the condition that the distance between the intelligent driving device and the target object is smaller than or equal to the preset threshold value, the luminous intensity of the illuminating lamp can be adjusted to be the second luminous intensity, and the second luminous intensity can be weaker luminous intensity, so that the influence on the target objects such as pedestrians and vehicles can be reduced.

In one possible implementation manner, a target luminous intensity corresponding to the distance between the intelligent driving device and the target object may be determined according to the corresponding relationship of the target distance and the distance between the intelligent driving device and the target object, and then the luminous intensity of the illuminating lamp of the intelligent driving device may be adjusted to the target luminous intensity.

In this implementation, the smart driving device may obtain a pre-stored correspondence between the target distance and the target luminous intensity, and then query the target luminous intensity corresponding to the determined distance between the target object and the smart driving device according to the correspondence. Here, the target distance and the target luminous intensity may be a piecewise function, for example, when the target distance is less than the first distance, the target luminous intensity may be a first luminous intensity, and when the target distance is greater than or equal to the first distance, the target luminous intensity may be a second luminous intensity, so that the target luminous intensity corresponding to the distance between the target object and the smart driving device may be quickly determined through the correspondence between the target distance and the target luminous intensity. Here, the target distance and the target luminous intensity may also be a continuous function, so that the luminous intensity of the illumination lamp of the intelligent driving device may be continuously changed according to the distance between the target object and the intelligent driving device.

Therefore, the target luminous intensity of the illuminating lamp of the current intelligent driving equipment can be quickly determined through the pre-stored corresponding relation between the target distance and the target luminous intensity, the illuminating lamp can be automatically adjusted according to the distance between the target object and the intelligent driving equipment, a proper illumination environment is provided for vehicles, and traffic accidents are reduced.

Here, the correspondence relationship between the target distance and the target light emission intensity may be determined by a large amount of data obtained by scene simulation. For example, in the case that the target distance is a certain distance, the acceptable light emitting intensity of the pedestrian or the driver at the target distance is determined, and the determined acceptable light emitting intensity may be used as the target light emitting intensity corresponding to the target distance. In some implementations, after determining the acceptable luminous intensity for the pedestrian or the driver at a certain target distance, the acceptable luminous intensity may be further adjusted, for example, the acceptable luminous intensity is decreased by a certain value, and the decreased luminous intensity is taken as the target luminous intensity corresponding to the target distance. Therefore, different sensitivities of pedestrians or drivers to light can be considered, and the influence of the light on the pedestrians or the drivers due to continuous reduction of the distance in the opposite driving process can be considered, so that the target luminous intensity corresponding to the target distance can be set to be smaller than the luminous intensity acceptable by the pedestrians or the drivers.

In one example of this implementation, it may be determined that the distance between the smart driving apparatus and the target object is in a first distance interval to which a list of correspondence relationships between target distances and target luminous intensities belongs, and then the target luminous intensity corresponding to the distance between the smart driving apparatus and the target object is determined according to the target luminous intensity corresponding to the first distance interval in the list. In this example, the correspondence between the target distance and the target light emission intensity may be represented by a list. The list can record a plurality of distance intervals and the luminous intensity or the luminous intensity coefficient corresponding to each distance interval, so that the luminous intensity or the luminous intensity coefficient corresponding to the first distance interval can be searched according to the first distance interval where the distance between the intelligent driving device and the target object is located, and the target luminous intensity corresponding to the distance between the intelligent driving device and the target object is determined. Here, in the case where the correspondence relationship between the distance section and the light emission intensity coefficient is recorded in the list, after the corresponding light emission intensity coefficient is obtained through the first distance section, the light emission intensity coefficient may be multiplied by the maximum light emission intensity of the smart driving apparatus to obtain the target light emission intensity corresponding to the distance between the smart driving apparatus and the target object.

For example, assuming that the lighting intensity coefficient of the maximum lighting intensity of the lighting lamp of the intelligent driving device is 1, and the lighting intensity coefficient when the lighting lamp does not light is 0, as shown in table 1, the target lighting intensity of the lighting lamp is provided with 6 gears, and the target lighting intensity coefficients are 0, 0.2, 0.4, 0.6, 0.8, and 1, respectively, the target lighting intensity corresponding to the distance between the intelligent driving device and the target object may be determined through the correspondence between the target distance and the target lighting intensity shown in table 1.

TABLE 1

Target distance (rice)	Target luminous intensity coefficient
		0-20	0.2
20-40	0.4
		40-70	0.6
70-120	0.8
		>120	1

In another example of this implementation, the second distance interval in which the distance between the smart driving device and the target object is located may be determined according to the distance between the smart driving device and the target object and a function representing a correspondence relationship between the target distance and the target luminous intensity. And then determining the target luminous intensity corresponding to the distance between the intelligent driving equipment and the target object according to the calculation mode of the target distance corresponding to the second distance interval in the function and the target luminous intensity. Here, the second distance interval is a distance interval of a function. The function may include a calculation manner of the luminous intensity or the luminous intensity coefficient corresponding to each distance interval, and the distance intervals may be multiple, so that a target calculation manner corresponding to a second distance interval may be searched according to the second distance interval in which the distance between the intelligent driving device and the target object is located, and the target luminous intensity corresponding to the distance between the intelligent driving device and the target object is calculated by using the target calculation manner.

For example, assuming that a target luminous intensity coefficient of the maximum luminous intensity of the illumination lamp of the smart driving apparatus is 1, a target luminous intensity coefficient when the illumination lamp is not illuminated is 0, and the target distance and the target luminous intensity coefficient may be expressed as a linear function as shown in equation (1):

where y may represent a target luminous intensity coefficient and x may represent a target distance. And determining the target luminous intensity corresponding to the distance between the intelligent driving equipment and the target object through a calculation mode of the target distance and the target luminous intensity coefficient shown in formula (1).

Through the light intensity adjusting scheme provided by the embodiment of the disclosure, the distance between target objects such as pedestrians and vehicles and the intelligent driving device can be determined, and the luminous intensity of the illuminating lamp can be automatically adjusted according to the distance, so that convenience can be provided for vehicle driving, the driving safety of the vehicle is improved, and traffic accidents are reduced. The process of obtaining the image coordinates of the target object is described below by way of an example.

Fig. 2 shows a flowchart of an example of determining image coordinates of a target object according to an embodiment of the present disclosure, which may include the steps of:

and step S21, performing feature extraction on the acquired image to obtain the image features of the image.

In this example, the neural network may be used to perform feature extraction on the acquired image, resulting in image features of the image. Here, the acquired image may be used as an input of a neural network, and the neural network is used to perform a convolution operation on the input image to obtain an image feature of the image. Or, the collected image may be preprocessed, for example, the image is preprocessed by scaling, clipping, rotating, and the like, and then the preprocessed image is input into the neural network, and the preprocessed image is convolved by the neural network to obtain the image features of the image, so that the efficiency and quality of image feature extraction can be improved.

In step S22, an image region belonging to the target feature class in the image is determined according to the image features of the image.

In this example, an image region belonging to a target feature class in the image may be determined according to the image features extracted by the neural network, and the target feature class may represent the image features of each class of target object, for example, in the case that the target object may be a pedestrian, the target feature class may be a feature class formed by the image features of the pedestrian. Here, the target object may include one or more of a pedestrian, a non-motor vehicle, and a motor vehicle, and accordingly, the target feature class may include one or more of a pedestrian feature class, a non-motor vehicle feature class, and a motor vehicle feature class.

When the target feature classes are multiple, the branch network of each target feature class may be used to perform convolution operation on the image features of the target image to obtain a detection result for the image region of each target feature class, and then at least one image region belonging to the multiple target feature classes in the target image is determined according to the detection results for the image regions of the multiple target feature classes.

Here, the neural network may include a plurality of branch networks, and each branch network may perform a convolution operation on the image features of the image in parallel to obtain a detection result for detecting the image area of each target feature class, where the detection result may be the image area where the target object of each target feature class is located. At least one image area belonging to a plurality of target feature classes can then be obtained by combining the detection results obtained from each of the branched networks.

For example, the neural network may include three branch networks, a first branch network may correspond to the pedestrian feature class, so that the image region of the pedestrian feature class may be detected by using the first branch network, and in a case that a pedestrian is detected in the target image, the image region of the pedestrian may be identified, for example, an image region where the pedestrian is located is identified by using a block, and the image region identified by the block may be a detection result of the image region of the pedestrian feature class. Similarly, the second branched network may correspond to the non-motor vehicle feature class, and the image region detection result for the non-motor vehicle feature class may be obtained by using the second branched network. The third branch network may correspond to the vehicle feature class, and the third branch network may be used to obtain an image region detection result for the vehicle feature class. And then, the detection results obtained by each branch network can be combined to finally obtain the image areas of the pedestrians, the motor vehicles and the non-motor vehicles identified by the boxes.

Step S23, determining image coordinates of the target object in the image according to the image area in the image belonging to the target feature class.

In this example, the image coordinates of the area center of the image area belonging to the target feature class in the image may be determined from the image coordinates of the image area, and the image coordinates of the area center may be determined as the image coordinates of the target object. Or, any one pixel point may be selected in the image region, the image coordinate of the pixel point is determined, and the image coordinate of the pixel point is determined as the image coordinate of the target object. Alternatively, an average image coordinate of the pixel points in the image region may be calculated, and the average image coordinate may be determined as the image coordinate of the target object.

It should be noted that the network structure of the neural network in the embodiments of the present disclosure is not limited, and may be any neural network having a target detection function, for example, a neural network having a network structure such as fast RCNN, SSD, YOLO, or the like. In addition, there is no special limitation on the number of network layers and the size of the neural network, so that the light intensity adjusting scheme provided by the embodiment of the present disclosure has high practicability, and can be used in any scene where the light intensity of the light needs to be adjusted.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.

In addition, the present disclosure also provides a light intensity adjusting device, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the light intensity adjusting methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are omitted for brevity.

It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.

Fig. 4 shows a block diagram of a light intensity adjusting apparatus according to an embodiment of the present disclosure, which includes, as shown in fig. 4:

the determining module 41 is configured to determine, according to an image acquired by an intelligent driving device, a distance between a target object in the image and the intelligent driving device;

and the adjusting module 42 is used for adjusting the luminous intensity of an illuminating lamp of the intelligent driving device according to the distance between the intelligent driving device and the target object.

In a possible implementation manner, the determining module 41 is configured to determine, according to the image, a position of a target object in the image; and determining the distance between the target object and the intelligent driving equipment according to the position of the target object.

In a possible implementation manner, the determining module 41 is configured to, when determining the position of the target object in the image according to the image, include:

In one possible implementation, the determining module 41 is configured to, when determining the position of the target object in the image based on the image coordinates of the target object in the image, include:

the determining module 41 is configured to, when determining the distance between the target object and the smart driving device according to the position of the target object, include: and determining the distance between the target object and the intelligent driving device according to the world coordinates of the target object and the world coordinates of the intelligent driving device.

In a possible implementation, the determining module 41 is further configured to determine the coordinate transformation relationship by:

acquiring an annotation image;

In a possible implementation manner, the determining module 41 is configured to determine, according to the image, the number of reference objects between the target object and the smart driving device; determining the distance between the intelligent driving equipment and the target object according to the number of the reference objects between the target object and the intelligent driving equipment and the distance between the adjacent reference objects; wherein the distance between two adjacent reference objects is known.

In a possible implementation manner, the target objects are multiple, and the adjusting module 42 is configured to adjust the light emitting intensity of the illumination lamp of the smart driving device according to the minimum distance between the multiple target objects and the smart driving device.

In a possible implementation manner, the adjusting module 42 is configured to determine a target light-emitting intensity corresponding to a distance between the smart driving device and the target object according to a corresponding relationship between a target distance and a target light-emitting intensity and a distance between the smart driving device and the target object; and adjusting the luminous intensity of an illuminating lamp of the intelligent driving device to the target luminous intensity.

In a possible implementation manner, the adjusting module 42 is configured to, when determining a target luminous intensity corresponding to a distance between the smart driving device and the target object according to a correspondence between a target distance and a target luminous intensity and a distance between the smart driving device and the target object, include:

In a possible implementation manner, the determining module 41 is further configured to determine, according to the image, that a target object does not exist in the image;

the adjusting module 42 is further configured to adjust the light intensity of the illuminating lamp of the intelligent driving device to a preset light intensity, or keep the light intensity of the illuminating lamp of the intelligent driving device unchanged.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured as the above method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 5 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 5, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, relative light intensity adjustments of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 6 is a block diagram illustrating an electronic device 1900 according to an example embodiment. For example, the electronic device 1900 may be provided as a server. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method of adjusting light intensity, the method comprising:

2. The method of claim 1, wherein determining a distance between a target object in the image and a smart driving device from the image captured by the smart driving device comprises:

3. The method of claim 2, wherein determining the location of the target object in the image from the image comprises:

4. The method of claim 3, wherein determining the location of the target object in the image based on image coordinates of the target object in the image comprises:

5. The method of claim 4, wherein the coordinate transformation relationship is determined by:

acquiring an annotation image;

6. The method of claim 1, wherein determining a distance between a target object in the image and a smart driving device from the image captured by the smart driving device comprises:

7. The method according to any one of claims 1 to 6, wherein the target object is a plurality of objects, and the adjusting of the luminous intensity of an illumination lamp of the smart driving device according to the distance between the smart driving device and the target object comprises:

8. A light intensity adjusting apparatus, characterized in that the apparatus comprises:

9. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 7.

10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.