CN112995519A - Camera self-adaptive adjustment method and device for water detection monitoring - Google Patents
Camera self-adaptive adjustment method and device for water detection monitoring Download PDFInfo
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Abstract
The disclosure relates to a camera self-adaptive adjusting method and device for water detection monitoring. The method comprises the following steps: acquiring an image of the water exploration drilling machine acquired by a camera in real time; identifying a drill bit and a main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body; and rotating the camera according to the size of an included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0. The scheme that this disclosure provided can replace the manual work to realize spy water and detect, realizes long-range on-line discernment, when reducing the manual work, promotes the speed and the precision of fortune dimension greatly, changes traditional artifical identification method, has realized detecting intellectuality, compares simultaneously the former spy water detecting system, through the position of self-adaptation adjustment camera for detect more accurately.
Description
Technical Field
The disclosure relates to the field of intelligent monitoring, in particular to a camera self-adaptive adjusting method and device for water detection monitoring.
Background
The target detection is a hot direction of computer vision and digital image processing, is widely applied to various fields of robot navigation, intelligent video monitoring, industrial detection, aerospace and the like, reduces the consumption of human capital through the computer vision, and has important practical significance. Therefore, the target detection becomes a research hotspot of theory and application in recent years, is an important branch of image processing and computer vision discipline and is also a core part of an intelligent monitoring system, and simultaneously, the target detection is also a basic algorithm in the field of universal identity recognition and plays a vital role in subsequent tasks such as face recognition, gait recognition, crowd counting, instance segmentation and the like.
With the development and progress of society, the requirements and expectations of various industries on safe work reach unprecedented heights. In the actual production project water detection, the components used for water detection need to be identified, and meanwhile, the emergency situation needs to be pre-warned. The most common and effective method today is to judge frame by manually observing videos or judge by workers on a specific production operation site, and manual operation has the function that an automatic system cannot replace the manual operation, namely, effective emergency measures can be taken emergently for the objects.
However, the existing manual methods have large workload, are relatively hard and have overhigh cost; the timeliness of the remote manual method cannot be achieved, personnel arrangement is difficult to achieve, detection information cannot be transmitted in time, the detection speed is low, and the precision is poor.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a camera adaptive adjustment method and device for water detection monitoring, which aims to implement a system for tracking and detecting the problematic situations and performing real-time early warning in a water detection project, and simultaneously, the detection accuracy is improved by an adaptive camera.
According to a first aspect of the embodiments of the present disclosure, there is provided a camera adaptive adjustment method for water detection monitoring, including:
acquiring an image of the water exploration drilling machine acquired by a camera in real time;
identifying a drill bit and a main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
and rotating the camera according to the size of an included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
Further, according to the line of drill bit and the detection frame central point of main fuselage and the contained angle size of image coordinate system x axle are right the camera rotates, specifically includes:
when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is greater than 0, controlling the camera to rotate leftwards;
and when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is less than 0, controlling the camera to rotate rightwards.
Further, the method also includes:
comparing the sizes of the detection frames of the drill bit and the main body with a preset size;
and zooming the image collected by the camera according to the comparison result.
Further, the scaling the image collected by the camera according to the comparison result specifically includes:
when the size of the detection frame is larger than a preset size, carrying out reduction operation on the image;
and when the size of the detection frame is smaller than a preset size, carrying out amplification operation on the image.
According to a second aspect of the embodiments of the present disclosure, there is provided a camera adaptive adjustment device for monitoring water detection, including:
the image acquisition module is used for acquiring images of the water exploration drilling machine acquired by the camera in real time;
the target identification module is used for identifying the drill bit and the main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
and the camera adjusting module is used for rotating the camera according to the included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
Further, the camera adjustment module specifically includes:
the first adjusting unit is used for controlling the camera to rotate leftwards when an included angle between the connecting line and the positive direction of the x axis of the image coordinate system is larger than 0;
and the second adjusting unit is used for controlling the camera to rotate rightwards when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is less than 0.
Further, the apparatus further comprises:
the size comparison module is used for comparing the sizes of the drill bit and the detection frame of the main body with a preset size;
and the image scaling module is used for scaling the image collected by the camera according to the comparison result.
Further, the image scaling module specifically includes:
an image reducing unit configured to perform a reduction operation on the image when the size of the detection frame is larger than a preset size;
and the image amplifying unit is used for amplifying the image when the size of the detection frame is smaller than a preset size.
According to a third aspect of the embodiments of the present disclosure, there is provided a terminal device, including:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the method as described above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the intelligent water detection system can replace manual work to realize water detection, realize remote online identification, reduce manual work, simultaneously promote the speed and the precision of fortune dimension greatly, change traditional manual identification method, realized detecting intellectuality, simultaneously compare in the past the water detection system, through the position of self-adaptation adjustment camera for it is more accurate to detect.
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.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 is a schematic flowchart illustrating a camera adaptive adjustment method for water detection monitoring according to an exemplary embodiment of the present disclosure;
FIG. 2 is a view of the drill bit and main body and their labels detected by the target detection algorithm;
fig. 3 is an operation schematic diagram of a camera adaptive adjustment method according to an exemplary embodiment of the present disclosure;
FIG. 4 is a schematic diagram of the connection line of the center points of the detection frames of the drill bit and the main body forming an included angle with the x-axis of the image coordinate system;
FIG. 5 is a schematic diagram of the calculation of the angle between the connecting line of the center points of the drill and the main body and the x-axis of the image coordinate system;
FIG. 6 is a schematic diagram of the detection effect after the adjustment of the camera is completed;
fig. 7 is a block diagram illustrating a structure of a camera adaptive adjustment device for monitoring water detection according to an exemplary embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating a computing device, according to an example embodiment of the present disclosure.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means two or more unless specifically limited otherwise.
Technical solutions of embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart illustrating a camera adaptive adjustment method for water detection monitoring according to an exemplary embodiment of the present disclosure.
With reference to figure 1 of the drawings,
the method comprises the following steps:
110. acquiring an image of the water exploration drilling machine acquired by a camera in real time;
specifically, a high-definition camera can be installed above a water exploration drilling machine on a production site and used for clearly shooting the area of the whole drilling site and acquiring videos of the water exploration site in real time, and the camera transmits the videos to an analysis server in real time through a network.
120. Identifying a drill bit and a main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
specifically, the analysis server may start the recognition function corresponding to the position of the water detection component according to the acquired video stream, that is, detect the drill bit and the main body of the two components of water detection through a target detection algorithm to obtain the position of the camera in the image, that is, the detection frame, as shown in fig. 2, where the head and the machine respectively represent the drill bit and the main body.
In this embodiment, the neural network model may adopt a target detection algorithm labeled by a target object of supervised learning to identify a target labeled in the video image. The working principle of target object labeling for supervised learning is as follows:
1) according to videos of two different parts shot by a camera in different time periods, image samples with different sizes and positions are made;
2) marking parts in the image sample to manufacture a training sample;
3) training the target detection model by using the training sample to enable the weight data to be in accordance with expectations;
4) accordingly, the target detection model can be used to identify components contained in the video captured in real time by the camera in real time.
Preferably, the object detection model in this embodiment detects the object in the image in a frame-by-frame manner.
The working principle of target detection is as follows:
1) marking images of different frames in a video, marking a target to be detected, and dividing the target into a training set and a data set;
2) training and improving by using the marked sample to obtain a target detection model;
3) and detecting the pictures shot by the camera frame by frame through the target detection model.
As shown in fig. 3, the target detection model may adopt the detection model of YOLOv3, and the workflow thereof is as follows:
firstly, the received shot video is transmitted to an analysis server through a network, then the video is decomposed frame by frame, and each frame of picture is transmitted to a YOLOv3 network model to extract features. YOLOv3 is a full convolution network, largely uses the jump layer connection of residual errors, and uses the stride of conv to realize down-sampling, and uses the anchor mode to improve the detection accuracy. The position and size of the detected object are obtained through YOLOv3, and then the name of the detected object and the size and position information thereof are output to the business model.
130. And rotating the camera according to the size of an included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
Specifically, an included angle formed by a connecting line of the center points of the drill and the detection frame of the main body and an x-axis of the image coordinate system is θ, as shown in fig. 4.
As shown in FIG. 5, P1 and P2 are the center points of the detection frames of the drill bit and the main body, respectively, and the coordinates are (x)p1,yp1) And (x)p2,yp2) Then, the calculation formula of the included angle θ is as follows:
as shown in fig. 3, when the calculated included angle θ is greater than 0, the lens of the camera is left rotated; and when the calculated included angle theta is smaller than 0, performing right rotation operation on the lens of the camera. Like this, the camera can constantly change the position that detects through the calculated result of contained angle to make the better x axle that is close of contained angle of two detection objects, realize better testing result, the detection effect after the camera adjustment is accomplished is shown in fig. 6.
According to the embodiment of the disclosure, the camera is used for shooting the water detection part; the method comprises the steps of utilizing a target detection method to detect components in a video frame by frame, and self-adaptively adjusting the position of a camera according to the detected positions of a drill bit and a main body to enable an included angle between detected contents to be better parallel to an x axis, so that a better detection function is realized.
Optionally, in this embodiment, the method further includes:
140. comparing the sizes of the detection frames of the drill bit and the main body with a preset size;
150. and zooming the image collected by the camera according to the comparison result.
Specifically, as shown in fig. 3, the processing flow when the size of the object exceeds the range specifically includes: when the size of the object is too large, namely the size of the detection frame is larger than the preset size, reducing the image shot by the camera; when the size of the object is too small, that is, the size of the detection frame is smaller than a preset size, the image photographed by the camera is magnified.
Generally speaking, the embodiment of the disclosure converts manual observation into machine observation and detection through a target detection algorithm, so as to achieve the purposes of intelligent and automatic machine tracking and detection.
Fig. 7 is a block diagram illustrating a structure of a camera adaptive adjustment device for monitoring water detection according to an exemplary embodiment of the present disclosure.
With reference to figure 7 of the drawings,
the device includes:
the image acquisition module is used for acquiring images of the water exploration drilling machine acquired by the camera in real time;
the target identification module is used for identifying the drill bit and the main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
and the camera adjusting module is used for rotating the camera according to the included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
Optionally, in this embodiment, the camera adjustment module specifically includes:
the first adjusting unit is used for controlling the camera to rotate leftwards when an included angle between the connecting line and the positive direction of the x axis of the image coordinate system is larger than 0;
and the second adjusting unit is used for controlling the camera to rotate rightwards when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is less than 0.
Optionally, in this embodiment, the apparatus further includes:
the size comparison module is used for comparing the sizes of the drill bit and the detection frame of the main body with a preset size;
and the image scaling module is used for scaling the image collected by the camera according to the comparison result.
Optionally, in this embodiment, the image scaling module specifically includes:
an image reducing unit configured to perform a reduction operation on the image when the size of the detection frame is larger than a preset size;
and the image amplifying unit is used for amplifying the image when the size of the detection frame is smaller than a preset size.
FIG. 8 is a schematic diagram illustrating a computing device, according to an example embodiment of the present disclosure.
Referring to fig. 8, computing device 800 includes memory 810 and processor 820.
The Processor 820 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 810 may include various types of storage units, such as system memory, Read Only Memory (ROM), and permanent storage. Wherein the ROM may store static data or instructions for the processor 820 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. In addition, the memory 810 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, may also be employed. In some embodiments, memory 810 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, dual layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disc, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 810 has stored thereon executable code that, when processed by the processor 820, may cause the processor 820 to perform some or all of the methods described above.
The aspects of the present disclosure have been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that the acts and modules referred to in the specification are not necessarily required by the invention. In addition, it can be understood that steps in the method of the embodiment of the present disclosure may be sequentially adjusted, combined, and deleted according to actual needs, and modules in the device of the embodiment of the present disclosure may be combined, divided, and deleted according to actual needs.
Furthermore, the method according to the present disclosure may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present disclosure.
Alternatively, the present disclosure may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) that, when executed by a processor of an electronic device (or computing device, server, or the like), causes the processor to perform some or all of the various steps of the above-described method according to the present disclosure.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems and methods 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 code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A camera self-adaptive adjustment method for water detection monitoring is characterized by comprising the following steps:
acquiring an image of the water exploration drilling machine acquired by a camera in real time;
identifying a drill bit and a main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
and rotating the camera according to the size of an included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
2. The method according to claim 1, wherein rotating the camera according to an angle between a connecting line of the center points of the detection frames of the drill and the main body and an x-axis of an image coordinate system comprises:
when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is greater than 0, controlling the camera to rotate leftwards;
and when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is less than 0, controlling the camera to rotate rightwards.
3. The method of claim 1 or 2, further comprising:
comparing the sizes of the detection frames of the drill bit and the main body with a preset size;
and zooming the image collected by the camera according to the comparison result.
4. The method according to claim 3, wherein the scaling the image captured by the camera according to the comparison result specifically comprises:
when the size of the detection frame is larger than a preset size, carrying out reduction operation on the image;
and when the size of the detection frame is smaller than a preset size, carrying out amplification operation on the image.
5. A camera self-adaptation adjusting device for spy water monitoring which characterized in that includes:
the image acquisition module is used for acquiring images of the water exploration drilling machine acquired by the camera in real time;
the target identification module is used for identifying the drill bit and the main body of the water exploration drilling machine in the image through a pre-trained neural network model to obtain detection frames of the drill bit and the main body;
and the camera adjusting module is used for rotating the camera according to the included angle between the connecting line of the central points of the detection frames of the drill bit and the main body and the x axis of the image coordinate system, so that the included angle is 0.
6. The apparatus according to claim 5, wherein the camera adjustment module specifically includes:
the first adjusting unit is used for controlling the camera to rotate leftwards when an included angle between the connecting line and the positive direction of the x axis of the image coordinate system is larger than 0;
and the second adjusting unit is used for controlling the camera to rotate rightwards when the included angle between the connecting line and the positive direction of the x axis of the image coordinate system is less than 0.
7. The apparatus of claim 5 or 6, further comprising:
the size comparison module is used for comparing the sizes of the drill bit and the detection frame of the main body with a preset size;
and the image scaling module is used for scaling the image collected by the camera according to the comparison result.
8. The apparatus according to claim 7, wherein the image scaling module specifically comprises:
an image reducing unit configured to perform a reduction operation on the image when the size of the detection frame is larger than a preset size;
and the image amplifying unit is used for amplifying the image when the size of the detection frame is smaller than a preset size.
9. A terminal device, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-4.
10. A non-transitory machine-readable storage medium having executable code stored thereon, wherein the executable code, when executed by a processor of an electronic device, causes the processor to perform the method of any of claims 1-4.
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