CN110929676A - Deep learning-based real-time detection method for illegal turning around - Google Patents
Deep learning-based real-time detection method for illegal turning around Download PDFInfo
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Abstract
The invention discloses a deep learning-based real-time detection method for illegal turning around, which comprises the following steps: 1) setting a camera preset position and calibrating a camera; 2) initializing a convolutional neural network model; 3) acquiring a real-time video stream; 4) checking the working state of the camera; 5) detecting a vehicle target by using a convolutional neural network model; 6) tracking a vehicle target; 7) converting the state of a vehicle tracking target; 8) analyzing the turning around of the violation; 9) and reporting the violation turning around. The invention provides a method for detecting the illegal turning around in real time based on deep learning, which has stronger robustness to environmental change, realizes the real-time detection effect and higher illegal turning around identification precision, simplifies the evidence obtaining work and greatly reduces the cost of human resources.
Description
Technical Field
The invention relates to the technical field of deep learning, in particular to a method for detecting violation turning around in real time based on deep learning.
Background
In recent years, with the popularization of automobiles, the pace of life of people is accelerated, the awareness of traffic safety is deficient, and illegal turning-around behaviors are frequently generated. The illegal turning around is mainly realized by forcibly turning around at intersections where turning around is not allowed. The behavior often causes traffic jam easily, affects the traveling efficiency of people, even more causes serious traffic accidents, and seriously harms the traveling safety of people. Therefore, it is important to accurately detect the illegal turning behavior of the vehicle in real time.
At present, the mode of detecting the violation turning around mainly comprises three modes, the first mode is manual detection of the violation turning around behavior, such as on-site law enforcement of traffic management personnel or manual capturing of the violation turning around behavior by utilizing video monitoring, but the manual mode cannot ensure all-weather inspection and supervision, and has complex work and high labor cost; the second is that sensors such as ground induction lines are used for detecting the illegal turning behavior, but the construction of the ground induction lines needs to damage the road surface, and the construction is complex, easy to damage and difficult to repair; the third is that the computer vision technology is used for realizing automatic detection of illegal turning behavior, at present, most of the traditional methods are used for vehicle detection, the use limitation exists, the phenomena of rain and fog, camera shake, light change and the like can affect the scene, the detection effect is further directly affected, and the robustness of the algorithm is poor.
Disclosure of Invention
In order to overcome the defects of low detection precision and low detection speed in the prior art, the invention provides a method for detecting the violation turning around in real time based on deep learning, which uses the CNN characteristics of a deep convolutional neural network to detect a vehicle target and integrates the video time-space continuity to accurately and quickly detect the violation turning around.
In order to realize the invention, the technical scheme is as follows:
a violation turning around real-time detection method based on deep learning is characterized by comprising the following steps:
1) setting a camera preset position and calibrating a camera;
2) initializing a convolutional neural network model;
3) acquiring a real-time video stream;
4) checking the working state of the camera;
5) detecting a vehicle target by using a convolutional neural network model;
6) tracking a vehicle target;
7) converting the state of a vehicle tracking target;
8) analyzing the turning around of the violation;
9) and reporting the violation turning around.
The real-time detection method for the violation turning around based on the deep learning is characterized in that in the step 1), the preset position of the camera is a fixed position where the camera is located when the violation turning around detection is carried out.
The method for detecting the illegal turning around in real time based on the deep learning is characterized in that in the step 1), the camera preset position is set to be a position for adjusting the camera to be suitable for detecting the illegal turning around, and the current camera position is set to be the preset position.
The real-time detection method for the illegal turning around based on the deep learning is characterized in that in the step 1), the camera is calibrated by intercepting a frame of image of a camera video stream, and calibrating a lane line, a passing detection area, a illegal turning around detection area, an illegal turning around starting line, an illegal turning around intermediate line and an illegal turning around ending line is carried out on the image.
The method for detecting the violation turning around in real time based on deep learning is characterized in that in the step 2), the convolutional neural network model is initialized to be a convolutional network model which is loaded into a GPU (graphics processing unit) display card and weight parameters are recovered.
The method for detecting the illegal turning around in real time based on deep learning is characterized in that in the step 4), the step of checking the working state of the camera specifically comprises the following steps:
4.1, acquiring the position information of the current camera, comparing the position information with a preset position to determine whether the position information is the same as the preset position, and if so, carrying out illegal turning detection; otherwise, carrying out illegal turning detection;
4.2 calculating the current video frame time T according to the formula (1)curAnd the previous frame time TpreTime interval T ofspaceThe units are seconds; if Tspace≥T0Resetting the target tracking queue; otherwise, carrying out normal illegal turning detection; t is0Represents a time threshold in seconds;
Tspace＝Tcur-Tpre(1)
the method for detecting the illegal turning around in real time based on deep learning is characterized in that in the step (6), the vehicle target tracking comprises the following steps:
6.1, matching the vehicle detection result with the tracking target:
6.1.1 calculating IoU sum of width and height errors E of the vehicle object D detected by the current frame and the object Q in the tracking queuew、Eh(ii) a IoU are calculated according to equation (2); wide height error Ew、EhRespectively calculating according to a formula (3) and a formula (4);
wherein D isboxBounding box, Q, of vehicle object D for the current frameTrackingBoxFor tracking real-time boundary box of target Q in queue, ∩ is intersection, ∪ is union, DwAnd DhWidth and height Q of real-time tracking bounding box for vehicle target D of current framewAnd QhRespectively tracking the width and the height of a bounding box of the target Q in the tracking queue in real time;
6.1.2 if IoU is more than or equal to IoUt，Ew≤Ewt，Eh≤EhtIf the vehicle is detected, the D and the Q are considered to be the same vehicle, the D is marked as matched, and the Q is marked as detected; IoU thereint，Ewt，EhtIoU for a preset adjustable valuetHas a value range of 0 to 1.0, EwtAnd EhtThe value of (a) is required to be greater than or equal to 0;
6.2, target tracking and updating:
6.2.1: adding an unmatched vehicle target D in a vehicle passing detection area into a tracking queue, wherein an initial boundary frame and a real-time tracking frame are boundary frames of a current target D, an initial image is a current video frame, and initial time Q isinit_timeThe state quantity is marked as start for the time of the current video frame;
6.2.2: if the detected tracking Q is marked, the tracking mode is IoU tracking, and the boundary box of the target D matched with the real-time tracking box is recorded; if the mark is the detected tracking Q, the tracking mode is KCF tracking, and a KCF algorithm is used for updating the real-time tracking boundary box;
6.3, maintaining a tracking queue:
6.3.1: if the tracking target in the tracking queue is not in the illegal turning detection area, removing the tracking target;
6.3.2: if the tracking target Q is not detected in the current frame and the front E exists in the tracking queuetolerate_frameIf the frame is not updated, the target is considered to be driven away, and the tracking target Q is removed from the tracking queue; wherein Etolerate_frameIs a preset adjustable value with the value range of Etolerate_frame≥1。
The method for detecting the illegal turning around in real time based on deep learning is characterized in that in the step 7), the vehicle tracking target state conversion detection comprises the following steps:
7.1: if the state quantity of the tracking target is start and the center point of the real-time tracking frame drives through the violation turning starting line, the state quantity is going1, and the first image of the tracking target is set as the current video image;
7.2: if the state quantity of the tracking target is going1 and the boundary of the real-time tracking frame passes through the violation turning middle line, the state quantity is going2, and the vehicle close-up image of the tracking target is set as the target real-time tracking frame area of the current video image and can be used for further license plate recognition in the later period and the like;
7.2: if the state quantity of the tracking target is going2 and the center point of the real-time tracking frame drives through the violation turning intermediate line, the state quantity is going3, the tracking mode is set to be IoU tracking, and the second image of the tracking target is set to be the current video image;
7.3: if the state quantity of the tracking target is going3 and the center point of the real-time tracking frame passes through the violation end-of-head ending line, the state quantity becomes end and the third image of the tracking target is set as the current video image.
The real-time detection method for the violation turning around based on the deep learning is characterized in that in the step 8), the violation turning around is analyzed to be a traversal tracking queue, if the state quantity of a tracking target is end, it is judged that the tracking target has the violation turning around behavior, reporting is carried out, and finally the tracking target is removed from the tracking queue.
The real-time detection method for the violation turning around based on the deep learning is characterized in that in the step 8), the violation turning around is reported by drawing the position of the target on three images of the violation turning around target, adding video frame time and location information on the bottom edges of the three images, arranging and combining the three images and a sketch image in a left-right mode to form a four-in-one violation synthetic image, and finally reporting the violation turning around information and the four-in-one violation synthetic image.
Compared with the prior art, the invention has the main beneficial effects that:
the invention provides a method for detecting the violation turning around in real time based on deep learning, which adopts an advanced deep learning technology and a simple and effective state transition discrimination mode, has stronger robustness to environmental change, and realizes real-time detection effect and higher violation turning around recognition precision. The method can effectively monitor the on-site violation turning behavior in all weather by using video monitoring, and meanwhile, the automatic four-in-one violation synthetic graph simplifies the evidence obtaining work, greatly reduces the human resource cost, ensures the traffic safety and improves the traffic efficiency.
Drawings
FIG. 1 is a flow chart of the steps of the method of the present invention;
FIG. 2 is a state transition diagram of the method of the present invention;
FIG. 3 is an example of a calibration chart of the method of the present invention;
fig. 4 is a view of the effect of the violation turning around of the method of the present invention.
In the figure: 1-passing detection area, 2-turning around start line against regulations, 3-lane line, 4-turning around ending line against regulations, 5-turning around middle line against regulations, 6-turning around detection area against regulations, and 7-target vehicle with turning around against regulations.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1 to 4, the method for detecting a violation turning around in real time based on deep learning provided by this embodiment includes the following steps:
9) and reporting the violation turning around.
S1, setting camera preset positions and calibrating the camera.
Specifically, the camera is adjusted to a proper position for carrying out illegal turning detection, and the current camera position is set as a preset position; then, a frame of image of the camera video stream is intercepted, and the calibration of a passing detection area 1, a violation turning starting line 2, a lane line 3, a violation turning ending line 4, a violation turning intermediate line 5 and a violation turning detection area 6 is carried out on the image.
And S2, initializing a convolutional neural network model.
Specifically, the convolutional network model is loaded into the GPU graphics card and the weight parameters are recovered.
And S3, acquiring the real-time video stream.
S4, checking the working state of the camera.
Specifically, the method comprises the following steps:
s4.1, acquiring the position information of the current camera, comparing the position information with a preset position to determine whether the position information is the same as the preset position, and detecting a congestion area if the position information is the same as the preset position; otherwise, not detecting the congestion area;
s4.2 calculating the current video frame time T according to the formula (1)curAnd the previous frame time TpreTime interval T ofspaceThe units are seconds; if Tspace≥T0Resetting the target tracking queue; otherwise, carrying out normal illegal turning detection; t is0Representing a time threshold in seconds.
Tspace＝Tcur-Tpre(1)
And S5, detecting the vehicle target by using the convolutional neural network model.
And S6, tracking the vehicle target.
Specifically, the method comprises the following steps:
s6.1, matching a vehicle detection result with a tracking target:
s6.1.1 calculating IoU and width-height error E of vehicle object D detected by current frame and object Q in tracking queuew、Eh(ii) a IoU are calculated according to equation (2); wide height error Ew、EhRespectively calculating according to a formula (3) and a formula (4);
wherein D isboxBounding box, Q, of vehicle object D for the current frameTrackingBoxFor tracking real-time boundary box of target Q in queue, ∩ is intersection, ∪ is union, DwAnd DhWidth and height Q of real-time tracking bounding box for vehicle target D of current framewAnd QhRespectively tracking the width and the height of a bounding box of the target Q in the tracking queue in real time;
s6.1.2 if IoU is not less than IoUt，Ew≤Ewt，Eh≤EhtIf the vehicle is detected, the D and the Q are considered to be the same vehicle, the D is marked as matched, and the Q is marked as detected; IoU thereint，Ewt，EhtIoU for a preset adjustable valuetHas a value range of 0 to 1.0, EwtAnd EhtThe value of (a) is required to be greater than or equal to 0;
s6.2, target tracking and updating:
s6.2.1: adding an unmatched vehicle target D in a vehicle passing detection area into a tracking queue, wherein an initial boundary frame and a real-time tracking frame are boundary frames of a current target D, an initial image is a current video frame, and initial time Q isinit_timeThe state quantity is marked as start for the time of the current video frame;
s6.2.2: if the detected tracking Q is marked, the tracking mode is IoU tracking, and the boundary box of the target D matched with the real-time tracking box is recorded; if the mark is the detected tracking Q, the tracking mode is KCF tracking, and a KCF algorithm is used for updating the real-time tracking boundary box;
s6.3, maintaining a tracking queue:
s6.3.1: if the tracking target in the tracking queue is not in the illegal turning detection area, removing the tracking target;
s6.3.2: if the tracking target Q is not detected in the current frame and the front E exists in the tracking queuetolerate_frameIf the frame is not updated, the target is considered to be driven away, and the tracking target Q is removed from the tracking queue; wherein Etolerate_frameIs a preset adjustable value with the value range of Etolerate_frame≥1；
S7, vehicle tracking target state conversion
Specifically, the method comprises the following steps:
s7.1: if the state quantity of the tracking target is start and the center point of the real-time tracking frame drives through the violation turning starting line, the state quantity is going1, and the first image of the tracking target is set as the current video image;
s7.2: if the state quantity of the tracking target is going1 and the boundary of the real-time tracking frame passes through the violation turning middle line, the state quantity is going2, and the vehicle close-up image of the tracking target is set as the target real-time tracking frame area of the current video image and can be used for further license plate recognition in the later period and the like;
s7.2: if the state quantity of the tracking target is going2 and the center point of the real-time tracking frame drives through the violation turning intermediate line, the state quantity is going3, the tracking mode is set to be IoU tracking, and the second image of the tracking target is set to be the current video image;
s7.3: if the state quantity of the tracking target is going3 and the center point of the real-time tracking frame passes through the violation end-falling ending line, the state quantity is changed to end, and the third image of the tracking target is set as the current video image;
s8, analysis of illegal turning around
Specifically, traversing the tracking queue, if the state quantity of the tracking target is end, judging that the tracking target has a violation turning behavior, reporting, and finally removing the tracking target from the tracking queue;
s8, reporting the illegal turning around
Specifically, the position of the target, namely the target vehicle 7 with the violation turning around is drawn on the three images of the violation turning around target, the three images and a sketch map are arranged and combined in pairs in a left-right direction to form a four-in-one violation composite map after video frame time and location information is added to the bottom edges of the three images, and finally the violation turning around information and the four-in-one violation composite map are reported.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A violation turning around real-time detection method based on deep learning is characterized by comprising the following steps:
1) setting a camera preset position and calibrating a camera;
2) initializing a convolutional neural network model;
3) acquiring a real-time video stream;
4) checking the working state of the camera;
5) detecting a vehicle target by using a convolutional neural network model;
6) tracking a vehicle target;
7) converting the state of a vehicle tracking target;
8) analyzing the turning around of the violation;
9) and reporting the violation turning around.
2. The method for detecting the illegal turning around based on the deep learning as claimed in claim 1, wherein in the step 1), the preset position of the camera is a fixed position where the camera is located when the illegal turning around detection is carried out.
3. The method for detecting the illegal turning around based on the deep learning as claimed in claim 1, wherein in the step 1), the camera preset position is set as a position for adjusting the camera to be suitable for illegal turning around detection, and the current camera position is set as the preset position.
4. The method for detecting the violation turning around in real time based on the deep learning as claimed in claim 1, wherein in the step 1), the camera is calibrated to intercept one frame of image of the camera video stream, and the calibration of a lane line, a passing vehicle detection area, a violation turning around starting line, a violation turning around middle line and a violation turning around ending line is performed on the image.
5. The deep learning-based violation turning around real-time detection method according to claim 1, wherein in the step 2), the convolutional neural network model is tiny-YOLO-v3, and the convolutional neural network model is initialized to be loaded into a GPU graphics card and weight parameters are restored.
6. The method for detecting the illegal turning around in real time based on the deep learning as claimed in claim 1, wherein in the step 4), the step of checking the working state of the camera specifically comprises the following steps:
4.1, acquiring the position information of the current camera, comparing the position information with a preset position to determine whether the position information is the same as the preset position, and if so, carrying out illegal turning detection; otherwise, carrying out illegal turning detection;
4.2 calculating the current video frame time T according to the formula (1)curAnd the previous frame time TpreTime interval T ofspaceThe units are seconds; if Tspace≥T0Resetting the target tracking queue; otherwise, carrying out normal illegal turning detection; t is0Represents a time threshold in seconds;
Tspace＝Tcur-Tpre(1)
7. the method for detecting the illegal turning around in real time based on the deep learning as claimed in claim 1, wherein in the step (6), the step of tracking the vehicle target comprises the following steps:
6.1, matching the vehicle detection result with the tracking target:
6.1.1 calculating IoU sum of width and height errors E of the vehicle object D detected by the current frame and the object Q in the tracking queuew、Eh(ii) a IoU are calculated according to equation (2); wide height error Ew、EhRespectively calculating according to a formula (3) and a formula (4);
wherein D isboxBounding box, Q, of vehicle object D for the current frameTrackingBoxFor tracking real-time boundary box of target Q in queue, ∩ is intersection, ∪ is union, DwAnd DhWidth and height Q of real-time tracking bounding box for vehicle target D of current framewAnd QhRespectively tracking the width and the height of a bounding box of the target Q in the tracking queue in real time;
6.1.2 if IoU is more than or equal to IoUt，Ew≤Ewt，Eh≤EhtIf the vehicle is detected, the D and the Q are considered to be the same vehicle, the D is marked as matched, and the Q is marked as detected; IoU thereint，Ewt，EhtIoU for a preset adjustable valuetHas a value range of 0 to 1.0, EwtAnd EhtThe value of (a) is required to be greater than or equal to 0;
6.2, target tracking and updating:
6.2.1: adding an unmatched vehicle target D in a vehicle passing detection area into a tracking queue, wherein an initial boundary frame and a real-time tracking frame are boundary frames of a current target D, an initial image is a current video frame, and initial time Q isinit_timeThe state quantity is marked as start for the time of the current video frame;
6.2.2: if the detected tracking Q is marked, the tracking mode is IoU tracking, and the boundary box of the target D matched with the real-time tracking box is recorded; if the mark is the detected tracking Q, the tracking mode is KCF tracking, and a KCF algorithm is used for updating the real-time tracking boundary box;
6.3, maintaining a tracking queue:
6.3.1: if the tracking target in the tracking queue is not in the illegal turning detection area, removing the tracking target;
6.3.2: if the tracking target Q is not detected in the current frame and the front E exists in the tracking queuetolerate_frameIf the frame is not updated, the target is considered to be driven away, and the tracking target Q is removed from the tracking queue; wherein Etolerate_frameIs a preset adjustable value with the value range of Etolerate_frame≥1。
8. The method for detecting the illegal turning around in real time based on the deep learning as claimed in claim 1, wherein in the step 7), the detection of the state transition of the vehicle tracking target comprises the following steps:
7.1: if the state quantity of the tracking target is start and the center point of the real-time tracking frame drives through the violation turning starting line, the state quantity is going1, and the first image of the tracking target is set as the current video image;
7.2: if the state quantity of the tracking target is going1 and the boundary of the real-time tracking frame passes through the violation turning middle line, the state quantity is going2, and the vehicle close-up image of the tracking target is set as the target real-time tracking frame area of the current video image and can be used for further license plate recognition in the later period and the like;
7.2: if the state quantity of the tracking target is going2 and the center point of the real-time tracking frame drives through the violation turning intermediate line, the state quantity is going3, the tracking mode is set to be IoU tracking, and the second image of the tracking target is set to be the current video image;
7.3: if the state quantity of the tracking target is going3 and the center point of the real-time tracking frame passes through the violation end-of-head ending line, the state quantity becomes end and the third image of the tracking target is set as the current video image.
9. The deep learning-based real-time detection method for the turning around of the violation, as set forth in claim 1, wherein in step 8), the analysis of the turning around of the violation is a traversal tracking queue, if the state quantity of the tracking target is end, it is determined that the tracking target has a turning around behavior against the violation, reporting is performed, and finally the tracking target is removed from the tracking queue.
10. The method for detecting the violation turning around in real time based on the deep learning of claim 1, wherein in the step 8), the violation turning around reporting is to draw the position of the target on three images of the violation turning around target, add video frame time and location information on the bottom edge of the three images, arrange and combine the three images and a sketch map into a four-in-one violation composite map in a left-right arrangement mode, and report the violation turning around information and the four-in-one violation composite map.
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| CN112820115A (en) * | 2021-01-05 | 2021-05-18 | 四川铁投信息技术产业投资有限公司 | Method for identifying steering state of running vehicle |
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| CN113257033A (en) * | 2021-07-01 | 2021-08-13 | 成都宜泊信息科技有限公司 | Parking lot management method and system, storage medium and electronic equipment |
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