CN115170536B

CN115170536B - Image detection method, training method and device of model

Info

Publication number: CN115170536B
Application number: CN202210869385.XA
Authority: CN
Inventors: 蒋旻悦; 何悦
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-07-22
Filing date: 2022-07-22
Publication date: 2023-05-05
Anticipated expiration: 2042-07-22
Also published as: CN115170536A

Abstract

The disclosure provides an image detection method and a training method and device of a model, relates to the technical field of artificial intelligence, in particular to the technical fields of image processing, computer vision, deep learning and the like, and especially relates to scenes such as smart cities and intelligent traffic. The implementation scheme is as follows: acquiring a first image, wherein the first image comprises a first area corresponding to a target object; performing feature extraction on the first image to obtain a first feature; and obtaining a detection result based on the first feature, the detection result indicating a position of the target object in the first image.

Description

Image detection method, training method and device of model

Technical Field

The disclosure relates to the technical field of artificial intelligence, in particular to the technical fields of image processing, computer vision, deep learning and the like, and particularly relates to scenes such as smart cities, intelligent transportation and the like, in particular to an image detection method, a training method of a model, a training device of the model, electronic equipment, a computer readable storage medium and a computer program product.

Background

Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.

Image detection based on artificial intelligence is performed by obtaining an image and detecting based on the image to obtain category and position information of an object in the image. How to improve the accuracy and precision of the obtained detection results is a constant concern.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.

Disclosure of Invention

The present disclosure provides an image detection method, a training method of a model, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

According to an aspect of the present disclosure, there is provided an image detection method including: acquiring a first image, wherein the first image comprises a first area corresponding to a target object; performing feature extraction on the first image to obtain a first feature, wherein the similarity between the first feature and a second feature is larger than a preset threshold, the second feature is obtained by performing feature extraction on a second image obtained based on the first image, the second image is obtained based on the first image and has a second region corresponding to the first region, and the contrast between the second region and other regions different from the second region in the second image is larger than the contrast between the first region and other regions different from the first region in the first image; and obtaining a detection result based on the first feature, the detection result indicating a position of the target object in the first image.

According to another aspect of the present disclosure, there is provided a training method of an image detection model, including: obtaining a sample image, wherein the sample image comprises a sample area corresponding to a target object; obtaining a training image based on the sample image, wherein the contrast between a training image area corresponding to the first sample area and other areas different from the training image area in the training image is larger than the contrast between the sample area and other areas different from the sample area in the sample image; inputting the sample image to the image detection model and inputting the training image to a trained first model; obtaining first features extracted by the image detection model based on the sample image and second features extracted by the training image based on the first model; obtaining a second loss based on the first feature and the second feature; and adjusting parameters of the image detection model based on the second loss.

According to another aspect of the present disclosure, there is provided an image detection apparatus including: an image acquisition unit configured to acquire a first image including a first region corresponding to a target object; a feature extraction unit configured to perform feature extraction on the first image to obtain a first feature, a similarity of the first feature to a second feature being larger than a preset threshold, the second feature being obtained by feature extraction on a second image obtained based on the first image and having a second region corresponding to the first region, and a contrast between the second region and other regions different from the second region in the second image being larger than a contrast between the first region and other regions different from the first region in the first image; and a detection result acquisition unit configured to obtain a detection result indicating a position of the target object in the first image based on the first feature.

According to another aspect of the present disclosure, there is provided a training apparatus of an image detection model, including: a sample image acquisition unit configured to obtain a sample image including a sample region corresponding to a target object; a training image acquisition unit configured to obtain a training image based on the sample image, a contrast between a training image region corresponding to the first sample region and other regions different from the training image region in the training image being greater than a contrast between the sample region and other regions different from the sample region in the sample image; an image input unit configured to input the sample image to the image detection model and the training image to a trained first model; a feature input unit configured to obtain a first feature extracted by the image detection model based on the sample image, and a second feature extracted based on the training image based on the first model, and a loss calculation unit configured to obtain a second loss based on the first feature and the second feature; and a parameter adjustment unit configured to adjust parameters of the image detection model based on the second loss.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method according to an embodiment of the present disclosure.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method according to embodiments of the present disclosure.

According to one or more embodiments of the present disclosure, accuracy of a detection result obtained after image detection of the first image may be improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates a flow chart of an image detection method according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of a training method of an image detection model according to an embodiment of the present disclosure;

FIG. 4 illustrates a flowchart of a process for obtaining a training image based on the sample image in a training method of an image detection model according to an embodiment of the present disclosure;

fig. 5 shows a block diagram of a structure of an image detection apparatus according to an embodiment of the present disclosure;

FIG. 6 shows a block diagram of a training apparatus of an image detection model according to an embodiment of the present disclosure; and

fig. 7 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another element. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.

The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the image detection method.

In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user

operating client devices

101, 102, 103, 104, 105, and/or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may receive the obtained detection results using the

client devices

101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that the present disclosure may support any number of client devices.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablet computers, personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays (such as smart glasses) and other devices. The gaming system may include various handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. For example only, the one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a blockchain network, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of

client devices

101, 102, 103, 104, 105, and/or 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and/or 106.

In some implementations, the server 120 may be a server of a distributed system or a server that incorporates a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and virtual private server (VPS, virtual Private Server) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 130 may be used to store information such as audio files and video files. Database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. Database 130 may be of different types. In some embodiments, the database used by server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve the databases and data from the databases in response to the commands.

In some embodiments, one or more of databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

According to an aspect of the present disclosure, there is provided an image detection method. As shown in fig. 2, an image detection method 200 according to some embodiments of the present disclosure includes:

step S210: acquiring a first image, wherein the first image comprises a first area corresponding to a target object;

step S220: performing feature extraction on the first image to obtain a first feature, wherein the similarity between the first feature and a second feature is larger than a preset threshold, the second feature is obtained by performing feature extraction on a second image obtained based on the first image, the second image is obtained based on the first image and has a second region corresponding to the first region, and the contrast between the second region and other regions different from the second region in the second image is larger than the contrast between the first region and other regions different from the first region in the first image; and

step S230: based on the first feature, a detection result is obtained, the detection result being indicative of a position of the target object in the first image.

In the related art, an image to be detected is input into a trained image detection model, so that the image detection model obtains a detection result based on the image characteristics of the image, wherein the image detection model is only obtained through supervised training by using a training sample and a labeling label of the training sample, and the generalization capability of the model is poor. When the image to be detected is blurred, the model often cannot obtain an accurate detection result based on the extracted features of the image.

In the embodiment according to the present disclosure, the first feature is obtained by performing feature extraction on the first image, the similarity between the first feature and the second feature obtained based on the second image is greater than the similarity threshold, and the contrast between the region of the target object in the second image and other regions different from the region is greater than that of the first image, that is, the target object in the second image is easier to distinguish, so that the image feature of the second image, which is easier to distinguish the target object, can be obtained based on the first image, and an accurate detection result is easier to obtain.

In some embodiments, the first image may be an image acquired by any imaging device.

In some embodiments, the target object may be any object to be detected contained in the first image.

In some embodiments, the first image is an image acquired by an onboard camera, and the target object comprises at least one of: lane lines, vehicles and traffic cones.

In some embodiments, the performing feature extraction on the first image, obtaining the first feature comprises:

inputting the target image into a first model, obtaining the first feature based on a feature extraction network of the first model, wherein,

The first model is obtained based on training based on a second model, wherein the first model takes as input a sample image and the second model takes as input a training image, wherein the sample image contains a sample region corresponding to the target object, the training image is obtained based on the sample image, and a contrast between a training image region corresponding to the sample region and other regions different from the training image region in the training image is greater than a contrast between the sample region and other regions different from the sample region in the sample image.

In the process of training the first model based on the second model, a sample image is input to the first model, and a training image which is obtained based on the sample image and has a contrast between the area where the target object is located and other areas larger than that of the sample image is input to the second model, so that the first model which is guided to be trained by the second model can extract image features which are extracted based on the sample image and the second model based on the training image, namely, the first model can extract image features of images with larger contrast between the area where the target object is located and other areas based on images with smaller contrast between the area where the target object is located and other areas, and the image features are features of images with larger contrast between the area where the target object is located and other areas, and the target object in the images can be separated more easily based on the image features, so that the obtained detection result is more accurate.

In some embodiments, the number of parameters of the first model is the same as the number of parameters of the second model.

In other embodiments, the number of parameters of the first model is smaller than the number of parameters of the second model.

Because the number of parameters of the second model is larger, the accuracy of the features obtained based on the training image is higher, and after the first model is trained based on the guidance of the second model, the features obtained by the first model also have the accuracy of the features obtained by the second model, so that a more accurate detection result can be obtained.

In some embodiments, the training image may be a salient image obtained after processing the sample image to highlight the sample region.

The highlighting image does not substantially change the sample image, only changes the brightness of the region where the target object is located, so that the target object can be highlighted and easily distinguished and detected.

In some embodiments, the processing of the sample image to highlight the sample region comprises at least one of:

increasing the brightness of the sample area; and

the brightness of other areas of the sample image than the sample area is reduced.

In some embodiments, the training image comprises a fused image obtained by:

Processing the sample image to highlight the sample area to obtain a third image; and

and fusing the third image and the sample image.

The training image is obtained by fusing the third image obtained after the sample image is subjected to the processing of the salient sample region and the sample image, so that the salient degree of the target object in the training image is lower than that of the third image, the situation that in the process of training the first model based on the second model, the difference between the image features extracted by the second model based on the training image and the image features extracted by the first model based on the sample image is overlarge is avoided, the first model cannot be converged is avoided, the process of training the first model is smooth, and the first model with better generalization is obtained.

In some embodiments, the fused image comprises a first scale of the sample image and a second scale of the third image, the sum of the first scale and the second scale being 1.

For example, the pixel value of the corresponding position in the fused image is obtained by adding the pixel value of each position in the first image multiplied by the first scale to the pixel value of the corresponding position in the third image multiplied by the second scale.

In some embodiments, the first ratio ranges from 0.1 to 0.9.

In one example, the first ratio is 0.3 and the second ratio is 0.7.

In some embodiments, the detection result is obtained by inputting the first feature into a classification network.

In some embodiments, the detection result also indicates a category of the target object. For example, when a plurality of target objects are included in the first image, the category of each target object can distinguish the target object from the plurality of target objects.

In some embodiments, the detection result is embodied as a segmented result.

According to another aspect of the present disclosure, there is also provided a training method of an image detection model, as shown in fig. 3, a method 300 includes:

step S310: obtaining a sample image, wherein the sample image comprises a sample area corresponding to a target object;

step S320: obtaining a training image based on the sample image, wherein the contrast between a training image area corresponding to the first sample area and other areas different from the training image area in the training image is larger than the contrast between the sample area and other areas different from the sample area in the sample image;

Step S330: inputting the sample image to the image detection model and inputting the training image to a trained first model; and

step S340: obtaining first features extracted by the image detection model based on the sample image and second features extracted by the training image based on the first model;

step S350: obtaining a second loss based on the first feature and the second feature; and

step S360: based on the second loss, parameters of the image detection model are adjusted.

In the related art, a supervised training image detection model is often performed based on a training sample and a sample label of the training sample, and the generalization capability of the model is poor. When the image to be detected is blurred, the image detection model often cannot obtain an accurate detection result based on the extracted features of the image.

According to an embodiment of the present disclosure, in training an image detection model based on a first model, a training image obtained based on the sample image in which a contrast between an area where a target object is located and other areas is greater than that of the sample image is input to the image detection model, so that in training the image detection model, data input in the image detection model and the first model are different. Because the contrast between the area where the target object is located in the training image and other areas is larger, namely the target image is easier to identify and detect in the training image, the first model can obtain an accurate detection result more easily based on the image features extracted from the training image.

After the first model is used for guiding the training image detection model, the image detection model can extract image features extracted from the first model based on the training image based on the input sample image, namely, the image detection model can be based on an image with smaller contrast between the region where the target object is located and other regions, and can extract image features extracted from the first model based on an image with larger contrast between the region where the target object is located and other regions, and accurate detection results can be obtained more easily based on the image features, so that the trained image detection model can obtain more accurate detection results.

In some embodiments, the sample image may be any image obtained by any imaging device.

In some embodiments, the target object may be any object to be detected.

In some embodiments, the sample image is an image acquired by an onboard camera, and the target object comprises at least one of: lane lines, vehicles and traffic cones.

In some embodiments, as shown in fig. 4, obtaining a training image based on the sample image comprises:

step S410: processing the sample image to highlight the sample area to obtain a highlighted image; and

Step S420: and obtaining the training image based on the salient image.

The highlighting image does not substantially change the sample image, only changes the brightness of the region where the target object is located, so that the target object can be highlighted and easily distinguished and detected. The training image is obtained based on the salient image, so that the detection result obtained based on the image features extracted from the training image is more accurate.

increasing the brightness of the sample area; and

In some embodiments, the salient image is taken as the training image.

In some embodiments, the obtaining the training image based on the salient image comprises:

and fusing the salient image and the sample image to obtain the training image.

The training image is obtained by fusing the salient image and the sample image, so that the target object in the training image is less highlighted than the salient image, the situation that in the process of training the image detection model based on the first model, the difference between the image features extracted by the first model based on the training image and the image features extracted by the image detection model based on the sample image is overlarge is avoided, the situation that the image detection model cannot converge is avoided, the process of training the image detection model is smooth, and the image detection model with better generalization is obtained.

In some embodiments, the training image comprises a first scale of the sample image and a second scale of the salient image, the sum of the first scale and the second scale being 1.

In some embodiments, the first ratio ranges from 0.1 to 0.9.

In one example, the first ratio is 0.3 and the second ratio is 0.7.

In some embodiments, the training method of the image detection model according to the present disclosure further comprises:

obtaining a label corresponding to the sample image, and obtaining a prediction result output by the image detection model;

obtaining a second loss based on the labeling tag and the prediction result; and

based on the second loss, parameters of the image detection model are adjusted.

In some embodiments, the annotation tag indicates the location of the target object in the sample image or the class of the target object.

Obtaining a first prediction result output by the image detection model and a second prediction result output by the first model;

obtaining a third loss based on the first prediction result and the second prediction result; and

based on the third loss, parameters of the image detection model are adjusted.

In some embodiments, the number of parameters of the image detection model is the same as the number of parameters of the first model.

In other embodiments, the number of parameters of the image detection model is smaller than the number of parameters of the first model.

Because the number of parameters of the first model is larger, the accuracy of the features obtained based on the training image is higher, and after the image detection model is trained based on the guidance of the first model, the features obtained by the image detection model also have the accuracy of the features obtained by the first model, so that a more accurate detection result can be obtained.

In some embodiments, in a method of image detection models according to the present disclosure, the first model is obtained by training with salient images.

According to another aspect of the present disclosure, there is provided an image detection apparatus, as shown in fig. 5, an apparatus 500 including: an image acquisition unit 510 configured to acquire a first image including a first region corresponding to a target object; a feature extraction unit 520 configured to perform feature extraction on the first image to obtain a first feature, a similarity of the first feature to a second feature being larger than a preset threshold, the second feature being obtained by feature extraction on a second image obtained based on the first image and having a second region corresponding to the first region, and a contrast between the second region and other regions different from the second region in the second image being larger than a contrast between the first region and other regions different from the first region in the first image; and a detection result acquisition unit 530 configured to obtain a detection result indicating a position of the target object in the first image based on the first feature.

In some embodiments, the feature extraction unit comprises: an image input unit configured to input the target image to a first model, obtain the first feature based on a feature extraction network of the first model, wherein the first model is obtained by training based on a second model, wherein the first model takes as input a sample image and the second model takes as input a training image, wherein the sample image contains a sample region corresponding to the target object, the training image is obtained based on the sample image, and a contrast between a training image region corresponding to the sample region and other regions different from the training image region in the training image is greater than a contrast between the sample region and other regions different from the sample region in the sample image.

In some embodiments, the training image comprises a fused image obtained by: processing the sample image to highlight the sample area to obtain a third image; and fusing the third image and the sample image.

In some embodiments, the processing of the sample image to highlight the sample region comprises at least one of: increasing the brightness of the sample area; and reducing the brightness of other areas of the sample image that are different from the sample area.

In some embodiments, the first ratio ranges from 0.1 to 0.9.

In some embodiments, the number of parameters of the second model is not less than the number of parameters of the first model.

In some embodiments, the first image comprises an image obtained by an onboard camera, and the target object comprises at least one of a lane line, a traffic cone.

According to another aspect of the present disclosure, there is also provided a training apparatus for an image detection model, as shown in fig. 6, an apparatus 600 includes: a sample image acquisition unit 610 configured to obtain a sample image including a sample region corresponding to a target object; a training image obtaining unit 620 configured to obtain a training image based on the sample image, wherein a contrast between a training image region corresponding to the first sample region and other regions different from the training image region in the training image is greater than a contrast between the sample region and other regions different from the sample region in the sample image; an image input unit 630 configured to input the sample image to the image detection model and the training image to a trained first model; a feature input unit 640 configured to obtain a first feature extracted by the image detection model based on the sample image, and a second feature extracted based on the training image based on the first model; a loss calculation unit 650 configured to obtain a second loss based on the first feature and the second feature; and a parameter adjustment unit 660 configured to adjust parameters of the image detection model based on the second loss.

In some embodiments, the training image acquisition unit comprises: a saliency processing unit configured to perform processing of salifying the sample region on the sample image to obtain a saliency image; and a training image acquisition subunit configured to obtain the training image based on the salient image.

In some embodiments, the training image acquisition subunit comprises: and a fusion unit configured to fuse the salient image and the sample image to obtain the training image.

In some embodiments, the first ratio ranges from 0.1 to 0.9.

In some embodiments, the sample image comprises an image obtained by an onboard camera, and the target object comprises at least one of: lane lines, traffic cones and vehicles.

According to embodiments of the present disclosure, there is also provided an electronic device, a readable storage medium and a computer program product.

Referring to fig. 7, a block diagram of an electronic device 700 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706, an output unit 707, a storage unit 708, and a communication unit 709. The input unit 706 may be any type of device capable of inputting information to the electronic device 700, the input unit 706 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and/or a remote control. The output unit 707 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 708 may include, but is not limited to, magnetic disks, optical disks. The communication unit 709 allows the electronic device 700 to exchange information/data with other devices through computer networks, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 700 via the ROM 702 and/or the communication unit 709. One or more of the steps of the method 200 described above may be performed when a computer program is loaded into RAM 703 and executed by the computing unit 701. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method 200 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely illustrative embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples but only by the claims and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.

Claims

1. An image detection method, comprising:

acquiring a first image, wherein the first image comprises a first area corresponding to a target object;

Performing feature extraction on the first image to obtain a first feature, wherein the similarity between the first feature and a second feature is larger than a preset threshold, the second feature is obtained by performing feature extraction on a second image obtained based on the first image, the second image is obtained based on the first image and has a second region corresponding to the first region, and the contrast between the second region and other regions different from the second region in the second image is larger than the contrast between the first region and other regions different from the first region in the first image; and

obtaining a detection result based on the first feature, the detection result indicating a position of the target object in the first image;

wherein the performing feature extraction on the first image, obtaining a first feature includes:

inputting the first image into a first model, obtaining the first feature based on a feature extraction network of the first model, wherein,

2. The method of claim 1, wherein the training image comprises a fused image obtained by:

and fusing the third image and the sample image.

3. The method of claim 2, wherein the processing of the sample image to highlight the sample region comprises at least one of:

increasing the brightness of the sample area; and

4. The method of claim 2, wherein the fused image comprises a first scale of the sample image and a second scale of the third image, the sum of the first scale and the second scale being 1.

5. The method of claim 1, wherein the number of parameters of the second model is not less than the number of parameters of the first model.

6. The method of claim 1, wherein the first image comprises an image obtained by an in-vehicle camera, the target object comprising: lane lines, vehicles or traffic cones.

7. A training method of an image detection model, comprising:

obtaining a sample image, wherein the sample image comprises a sample area corresponding to a target object;

obtaining a training image based on the sample image, wherein the contrast between a training image area corresponding to the sample area and other areas different from the training image area in the training image is larger than the contrast between the sample area and other areas different from the sample area in the sample image;

inputting the sample image to the image detection model and inputting the training image to a trained first model;

obtaining first features extracted by the image detection model based on the sample image and second features extracted by the training image based on the first model;

obtaining a second loss based on the first feature and the second feature; and

based on the second loss, parameters of the image detection model are adjusted.

8. The method of claim 7, wherein the obtaining a training image based on the sample image comprises:

processing the sample image to highlight the sample area to obtain a highlighted image; and

And obtaining the training image based on the salient image.

9. The method of claim 8, wherein the processing of the sample image to highlight the sample region comprises at least one of:

increasing the brightness of the sample area; and

10. The method of claim 8, wherein the obtaining the training image based on the salient image comprises:

and fusing the salient image and the sample image to obtain the training image.

11. The method of claim 10, wherein the training image comprises a first scale of the sample image and a second scale of the salient image, the sum of the first scale and the second scale being 1.

12. The method of claim 7, wherein the sample image is an image acquired by an in-vehicle camera, and the target object comprises: lane lines, vehicles or traffic cones.

13. An image detection apparatus comprising:

an image acquisition unit configured to acquire a first image including a first region corresponding to a target object;

A feature extraction unit configured to perform feature extraction on the first image to obtain a first feature, a similarity of the first feature to a second feature being larger than a preset threshold, the second feature being obtained by feature extraction on a second image obtained based on the first image and having a second region corresponding to the first region, and a contrast between the second region and other regions different from the second region in the second image being larger than a contrast between the first region and other regions different from the first region in the first image; and

a detection result acquisition unit configured to obtain a detection result indicating a position of the target object in the first image based on the first feature;

wherein the feature extraction unit includes:

an image input unit configured to input the first image to a first model, obtain the first feature based on a feature extraction network of the first model, wherein,

14. The apparatus of claim 13, wherein the training image comprises a fused image obtained by:

and fusing the third image and the sample image.

15. The apparatus of claim 14, wherein the processing of the sample image to highlight the sample region comprises at least one of:

increasing the brightness of the sample area; and

16. The apparatus of claim 15, wherein the fused image comprises a first scale of the sample image and a second scale of the third image, the sum of the first scale and the second scale being 1.

17. The apparatus of claim 13, wherein a number of parameters of the second model is not less than a number of parameters of the first model.

18. The apparatus of claim 13, wherein the first image comprises an image obtained by an in-vehicle camera, the target object comprising: lane lines, traffic cones or vehicles.

19. A training apparatus for an image detection model, comprising:

a sample image acquisition unit configured to obtain a sample image including a sample region corresponding to a target object;

a training image acquisition unit configured to obtain a training image based on the sample image, a contrast between a training image region corresponding to the sample region and other regions different from the training image region in the training image being greater than a contrast between the sample region and other regions different from the sample region in the sample image;

an image input unit configured to input the sample image to the image detection model and the training image to a trained first model;

a feature input unit configured to obtain a first feature extracted by the image detection model based on the sample image, and a second feature extracted based on the training image based on the first model, and

a loss calculation unit configured to obtain a second loss based on the first feature and the second feature; and

and a parameter adjustment unit configured to adjust parameters of the image detection model based on the second loss.

20. The apparatus of claim 19, wherein the training image acquisition unit comprises:

a saliency processing unit configured to perform processing of salifying the sample region on the sample image to obtain a saliency image; and

a training image acquisition subunit configured to obtain the training image based on the salient image.

21. The apparatus of claim 20, wherein the processing of the sample image to highlight the sample region comprises at least one of:

increasing the brightness of the sample area;

22. The apparatus of claim 20, wherein the training image acquisition subunit comprises:

and a fusion unit configured to fuse the salient image and the sample image to obtain the training image.

23. The apparatus of claim 22, wherein the training image comprises a first scale of the sample image and a second scale of the salient image, the sum of the first scale and the second scale being 1.

24. The apparatus of claim 19, wherein the sample image comprises an image obtained by an in-vehicle camera, the target object comprising: lane lines, traffic cones or vehicles.

25. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the method comprises the steps of

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.

26. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-12.