Gas inspection work compliance identification method based on target tracking algorithm

Abstract

The invention discloses a gas detection work compliance identification method based on a target tracking algorithm, and belongs to the technical field of gas detection. The method comprises the following steps: arranging a high-definition video camera in a methane detection area of a mine, acquiring video data in the methane detection area in real time, and preprocessing an image; a gas detector and a gas detector in a methane detection area in the video are identified through a YOLO target detection method; if a gas detector is detected to hold a gas detector and arrive at a specified methane detection area on time, judging that the operation is compliant operation, otherwise, judging that the operation is non-compliant operation; after it is verified that a gas detector holds a gas detector to arrive at a designated methane detection area on time, field gas detection work is tracked in real time through a CSRT target tracking algorithm; and comparing with the standardized operation, if the watt-hour test operation is in accordance with the standardized operation, determining that the operation is compliant operation, otherwise, determining that the operation is non-compliant operation. According to the invention, the reliability and safety of gas detection operation can be improved, and the safety risk caused by illegal operation or non-operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety monitoring and intelligent video analysis. Specifically, it is a method for identifying the compliance of gas inspection operations based on a target tracking algorithm, which is especially suitable for complex underground lighting environments. It realizes the automated supervision of the behavior trajectory tracking of gas inspectors, the verification of standardized action sequences, and the real-time warning of illegal operations, and belongs to the cross-application field of intelligent mine safety monitoring and industrial automation technology. Background Art

[0002] Gas is one of the serious threats in coal mine safety production, which can cause accidents such as combustion, explosion, and poisoning of personnel, resulting in casualties and economic losses. Gas detection is the core link in coal mine safety production. The traditional operation mode mainly relies on gas inspectors manually carrying portable detectors to measure the concentration along the inspection route. At present, manual inspection has problems such as strong subjectivity and incomplete supervision coverage, making it difficult to prevent illegal behaviors such as missed inspections (key areas not covered) and false inspections (simplifying processes or forging data). The underground production system is huge and the environment is complex and changeable. There are blind spots in the supervision of gas inspection operations. According to the statistics of the National Mine Safety Supervision Bureau, there are many non-standard problems in gas inspection operations during gas accidents. Therefore, how to effectively supervise gas inspection operations and ensure the compliance of gas inspection operations is a problem that needs to be solved in this technical field. Summary of the Invention

[0003] Aiming at the problems of missed inspections and false inspections in gas inspection operations in the prior art, the present invention provides a method for identifying the compliance of gas inspection operations based on a target tracking algorithm. This method intelligently monitors the whole process of gas inspection operations through computer vision technology, effectively improving the standardization of detection operations and the reliability of data. The specific technical solutions are as follows:

[0004] To solve the problems of missed inspections and false inspections in gas inspection operations, the present invention discloses a method for identifying the compliance of gas inspection operations based on a target tracking algorithm. It uses an intelligent video monitoring system to track the behavior and actions of gas inspectors in real time, automatically identify their behavior and actions, compare the behavior patterns of gas inspectors with the standard operation procedures to confirm whether each operation complies with the predetermined safety standards, and alarm the detected illegal behaviors to remind gas inspectors to correct them. This method can significantly reduce the safety risks caused by human errors or intentional illegal operations.

[0005] The technical solution adopted by the present invention is: a method for identifying the compliance of gas inspection operations based on a target tracking algorithm, including the following steps:

[0006] Step S1: Arrange high-definition video cameras in the methane detection area of the mine, collect video data in the methane detection area in real time, and preprocess the images;

[0007] Step S2: Identify the gas inspector and the gas detector in the methane detection area of the video through the YOLO object detection algorithm; if it is detected that the gas inspector holds the gas detector and arrives at the designated methane detection area on time, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation;

[0008] Step S3: After verifying that the gas inspector holds the gas detector and arrives at the designated methane detection area on time, perform real-time tracking of the on-site gas inspection operation through the CSRT object tracking algorithm; then compare it with the set standardized operation. If the gas inspection operation conforms to the standardized operation, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation; the standardized operation includes:

[0009] (1) Single gas inspection action process: Hold the gas detector high, squeeze the balloon by hand, and lower the head to read the value;

[0010] (2) Repeat the single detection action process according to the set number of times.

[0011] As a further improvement of the present invention, in step S1, the high-definition video camera has a wide dynamic range function. Let the light intensity range in the mine be I min to I max , and the wide dynamic range DR of the selected camera satisfies

[0012] As a further improvement of the present invention, in step S2, the method for judging whether the gas inspector arrives on time is to analyze the arrival time T of the gas inspector through the video image arrive and the specified arrival time T required . When T arrive >T required , it is determined that the gas inspection operation is non-compliant.

[0013] As a further improvement of the present invention, in step S3, the method for comparing the on-site gas inspection operation with the standardized operation is as follows:

[0014] (1) Judgment of the action of holding the gas detector high: Let the range of the methane detection area be R = (x min , y min , x max , y max ). When the gas detector is held high to this range (x, y) and satisfies x min ≤x≤x max and y min ≤y≤y max , confirm the execution of this action through object tracking;

[0015] (2) Judgment of the action of squeezing the balloon by hand: Use the object tracking algorithm to monitor the hand movement of the gas inspector. Let the initial position coordinates of the hand key point be and the position coordinates in the subsequent frame be By calculating the Euclidean distance When d t When the continuous satisfaction times reach the preset value N within the preset pinching distance range, it is determined that the hand pinching the balloon action is effective;

[0016] (3) Determination of the action of lowering the head to read the number: Let the initial value of the head posture angle be α0, and the angle becomes α during the reading action t , by calculating the angle difference Δα = α t -Δ0, when Δα is within the preset reading angle range, it is determined that the action of lowering the head to read the number is effective;

[0017] As a further improvement of the present invention, in step S3, the single-time gas inspection action process is repeated 3 times.

[0018] As a further improvement of the present invention, in step S3, the number of consecutive times of hand pinching the balloon is 5-6 times.

[0019] As a further improvement of the present invention, alarms are given for non-compliant operations in steps S2 and S3 to timely remind the monitoring personnel and the gas inspector.

[0020] As a further improvement of the present invention, the alarm includes visual signals, sound signals and electronic notifications.

[0021] The beneficial effects of the present invention are:

[0022] 1. The gas inspection operation compliance recognition method based on the target tracking algorithm constructed by the present invention has shown remarkable results in improving the standardization and supervision of gas inspection operations. By integrating target recognition and target tracking, strict standards and verification processes are established for each link of gas inspection operations.

[0023] (1) In the target recognition stage, by means of algorithms such as the convolutional neural network YOLO algorithm, the gas inspector and the gas detector carried by him can be accurately located;

[0024] (2) The target tracking technology ensures the accurate monitoring of the key actions of the gas inspector. For example, when the gas detector is held high, by setting the detection area range and combining coordinates to judge the position accuracy; when the balloon is pinched by hand, the algorithm monitors the position change of the hand joint points and judges whether it meets the requirements by calculating the Euclidean distance; when lowering the head to read the number, the accuracy of the action is confirmed by tracking the change of the head posture angle.

[0025] 2. The present invention can effectively reduce human errors and operation deviations. In traditional gas inspection operations, human factors are likely to lead to various non-compliant operations, such as incorrect judgment of the position of the gas detector, inconsistent number of times of pinching the balloon, incorrect recording of data, etc.; the automatic recognition and verification mechanism of this method can monitor the operation steps in real time, correct errors and deviations in time, and ensure strict compliance with safety standards.

[0026] 3. Real-time video monitoring and automatic data recording greatly enhance the transparency of operations. High-definition video cameras record the entire process of operations in real time in various environments, enabling management to directly understand the work situation of gas inspectors; the automatic data recording function not only records video information but also extracts and stores relevant operation data, facilitating management to access and analyze, helping to promptly identify problems and optimize operation processes.

[0027] 4. The present invention can effectively prevent safety hazards such as gas explosions. By monitoring the operations of gas inspectors in real time, it ensures the accuracy and timeliness of gas detection, avoiding the failure to detect gas accumulation due to missed or false detections. This measure protects the safety of staff and reduces economic losses caused by accidents.

[0028] 5. The real-time alarm mechanism improves the emergency response ability. Once the system identifies non-compliant operations, such as a gas inspector not repeating operations as required, incomplete operations, or incorrect data recording, it immediately triggers an alarm to notify supervisors and gas inspectors, enabling timely corrective measures to be taken to avoid accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0030] Figure 1 is the operation flow chart of the method for identifying compliance of gas inspection operations based on the object tracking algorithm of the present invention;

[0031] Figure 2 is the flow chart of the criteria for determining compliance of gas inspection operations;

[0032] Figure 3 is the feature map of the object tracking algorithm for gas inspection operations;

[0033] Figure 4 is the model architecture diagram of the object tracking algorithm for gas inspection operations. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Please refer to Figure 1 , the method for identifying compliance of gas inspection operations based on the object tracking algorithm of the present invention includes the following steps:

[0035] Step S1: Arrange high-definition video cameras in the methane detection area of the mine, and collect video data in the methane detection area in real time and preprocess the images.

[0036] The methane detection area is selected at key positions in the mine, such as areas where gas is likely to accumulate, the regular operation routes of gas inspectors, and near important equipment, etc. The high-definition video cameras obtain real-time images of the methane detection area.

[0037] 1. Due to the very dim light in the mine, this embodiment has relatively high requirements for high-definition video cameras to obtain images with better quality. The camera should have a wide dynamic range (WDR) function. Let the range of light intensity in the mine be I min to I max , the dynamic range DR of the selected camera should satisfy Configure the parameters of the camera, including resolution settings, such as 1920×1080; frame rate settings, such as 25 frames per second, focus and exposure settings, to ensure that clear images can be captured for subsequent analysis.

[0038] 2. Preprocess the real-time collected video images to provide more accurate input data for subsequent action recognition.

[0039] Specifically, collect video data in real time, and record the collected video stream as V(t), where t represents time.

[0040] (1) Image denoising processing

[0041] Adopt the mean filter denoising method. For each pixel point (x, y) in the image, the size of its neighborhood is set to n×n (such as 3×3), then the denoised pixel value P denoise (x, y) calculation formula is:

[0042]

[0043] where P(x+i, y+j) is the original value of the pixel point in the neighborhood.

[0044] (2) Image enhancement processing

[0045] Use histogram equalization to enhance the image contrast. Let the gray level range of the original image be [0, L-1], the total number of pixels be N, and the frequency of the gray level k appearing be nk, then its probability density function After histogram equalization, the calculation formula for the new gray level T(k) corresponding to the gray level is:

[0046] Step S2: Through the YOLO object detection method, identify the gas inspector and the gas detector in the methane detection area in the video; if it is detected that the gas inspector holds the gas detector and arrives at the designated methane detection area on time, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation.

[0047] To ensure that the gas inspector can arrive at the designated work location on time and carry out work with a gas detector, this system uses object detection image recognition technology, which focuses on identifying specific features in the image - namely, the behavior of the gas inspector carrying a gas detector.

[0048] This algorithm takes "the gas inspector holding a gas detector" as a key feature. This feature involves identifying the posture of the person and the object in their hand - the gas detector. In this way, the target object can be accurately identified from multiple similar image features. Let the person posture feature vector be The appearance feature vector of the gas detector is Then the combined feature vector is used to identify the gas inspector and the gas detector he carries.

[0049] This embodiment uses the convolutional neural network YOLO algorithm to implement object detection. This algorithm can quickly and accurately locate the people and gas detectors in the image when viewing the image once. Taking an image with a size of 480×640×3 from the gas inspection operation site as input, this image contains rough roadway wall textures, lighting equipment with different brightness and colors, and key target objects such as gas inspectors and gas detectors. The convolutional neural network YOLO algorithm implements object detection based on a unique grid division strategy, and the specific steps are as follows:

[0050] (1) Image grid division: The input image is evenly divided into S×S grids, where S = 13. Each grid cell (i,j) (where 0≤i<S, 0≤j<S) is responsible for detecting the target objects falling into this grid. This division method discretizes the entire image space, enabling the algorithm to search for and locate objects in different local areas.

[0051] (2) Grid cell prediction:

[0052] Each grid cell (i,j) is responsible for predicting B bounding boxes, where B = 2, as well as the confidence of these bounding boxes and C class probabilities. In the gas inspection operation scenario, C = 2.

[0053] For each grid cell (i,j), its prediction result is obtained through the calculation of the convolutional neural network. The size of the convolutional kernel is 3×3, and the convolutional operation at the grid cell (i,j) can be expressed by the following formula:

[0054]

[0055] where is the output feature value of the l - th convolutional kernel at the grid cell (i,j), is the weight of the l - th convolutional kernel at the position (m,n), b l is the corresponding bias term, I(i + m,j + n) represents the pixel value of the input image at the position (i + m,j + n), and * represents the convolutional operation.

[0056] The prediction result of the grid cell (i,j) can be represented as a tensor T ij , where p cis the probability of class c, where c = 1 represents the gas inspector and c = 2 represents the gas detector. (x, y, w, h) are the coordinates and dimensions of the bounding box. Here, the coordinates (x, y) are the offsets of the bounding box center relative to the grid cell, with values ranging from (0, 1), and w and h are the ratios relative to the entire image size. The calculation method is as follows:

[0057]

[0058] where f ij is the eigenvalue at the grid cell (i, j), w ck , w xk , w yk , w wk , w hk are the corresponding convolutional kernel weights, b c , b x , b yj , b w , b hz are the bias terms, σ is the activation function, such as the sigmoid function, and e is the natural exponential function, which is used to calculate the width and height of the bounding box.

[0059] Finally, YOLO outputs a tensor of size S×S×(B×5 + C), representing the prediction results for each grid cell in the entire image. In this embodiment, under the 13×13 grid division, the size of the output tensor is 13×13×(2×5 + 2). By analyzing this tensor, the bounding box information, class probabilities, etc. predicted for each grid cell can be obtained. Set the confidence threshold to 0.5, filter reliable detection results, and perform non-maximum suppression processing to remove bounding boxes with high overlap, so as to obtain the final accurate detection positions and class information of the gas inspector and the gas detector. These information will provide key basis for the subsequent compliance judgment of the gas inspection operation, helping the system accurately determine key compliance elements such as whether the gas inspector arrives at the designated place on time and whether they carry the necessary gas detection equipment, thereby effectively improving the safety and standardization of the entire gas inspection operation.

[0060] When the matching degree between the detected target feature vector and the predefined "gas inspector holding a gas detector" feature vector reaches the threshold θ, where θ is set to 0.8, it is confirmed that the gas inspector is on site.

[0061] Step S3, after verifying that the gas inspector arrives at the designated methane detection area on time with a gas detector, perform real-time tracking on the on-site gas inspection operation through the CSRT target tracking algorithm; then compare it with the standardized operation. If the gas inspection operation conforms to the standardized operation, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation. To ensure that the gas inspector follows the specified operation procedures during gas detection, this system uses an advanced target tracking algorithm to identify and verify key actions.

[0062] The technical implementation process of this step will be described in detail below. Please refer to Figures 2 - 4 .

[0063] 1. Action Definition and Recognition Target:

[0064] The system defines three key actions: the gas inspector holds up the gas detector, pinches the balloon with the hand, and lowers the head to read the value. These actions are regarded as the standardized operation steps of gas detection operations and need to be precisely executed and recognized.

[0065] 2. Deployment of the Target Tracking Algorithm:

[0066] Using the target tracking algorithm CSRT (Channel and Spatial Reliability Tracker) can track the actions of the gas inspector in real time in the video stream and maintain continuous visual continuity of the target. The CSRT algorithm searches for the region most similar to the target in the video frame by calculating the color histogram and spatial information of the target region.

[0067] (1) Determination of the Initial Target Region: In the actual scenario of gas inspection operations, first, through a target detection algorithm, such as the SSD or YOLO algorithm, the gas inspector or the gas detector is detected in the starting frame of the video stream, thereby determining the initial position of the target. Assume that the position and size of the gas inspector detected in the first frame are represented by (x0, y0, w0, h0) (where x0, y0 are the coordinates of the upper left corner of the target region, and w0, h0 are the width and height of the target region), and the initial region R0 of the target is defined accordingly. For example, in a video frame with a resolution of 480×640, the initial position of the gas inspector may be (100, 200, 50, 150), that is, starting from the coordinates (100, 200), a region with a width of 50 pixels and a height of 150 pixels.

[0068] (2) Color Space Quantization and Histogram Calculation: The pixels within the initial target region R0 are subjected to color space quantization, and the common RGB color space is quantized into 8×8×8 intervals. For each pixel within the region R0, its belonging quantization interval is determined according to its RGB value, and then the number of pixels within each interval is counted to obtain the color histogram H0 of the target region R0. This color histogram H0 will be used as the color feature representation of the target for subsequent searching for regions similar to the target color in the video frame.

[0069] (3) Extraction of Spatial Information: In addition to the color feature, the spatial information of the initial target region is also extracted, including its position (x0, y0) and size (w0, h0). These spatial information play an important role in the target tracking process and are used together with the color feature to accurately identify the position change of the target in subsequent frames.

[0070] (4) Video stream data acquisition: Continuously acquire the video stream of the gas inspection operation scene collected by the high-definition video cameras arranged in the mine. Each frame of the video stream will be used as the input data for object tracking. The image size is consistent with the resolution configured by the camera, such as 480×640×3 (where 3 represents the three RGB color channels), and the frame rate, such as 25 frames per second, determines the time resolution of object tracking, that is, there are 25 opportunities per second to update the position information of the object.

[0071] 3. Algorithm

[0072] (1) Construction and significance of the objective function: The CSRT algorithm searches for the region most similar to the object in the video frame by calculating the color histogram and spatial information of the object region. Let the position of the object in the t-th frame be R t =(x t , y t , w t , h t )(position coordinates and width and height), and updates the object position by minimizing the objective function . Among them, p i is the feature of pixel i in the candidate region R t (including color histogram and spatial information), p t is the object feature (that is, the color histogram H0 and spatial information (x0, y0, w0, h0) of the initial object region R0), w i is the weight, ρ is the distance metric function (such as the Bhattacharyya distance), and λ is the regularization parameter.

[0073] (2) Feature extraction and representation: For each pixel i in the candidate region R t , its feature p i includes color histogram features and spatial information features. The color histogram features are obtained by the same quantization method as calculating the color histogram of the initial object region R0, and the spatial information features include the relative position coordinates of pixel i in the candidate region R t . For example, for a pixel i in the candidate region R t , its color histogram feature may be an 8×8×8 vector representing the distribution of the pixel color in each quantization interval, and its spatial information feature may be a two-dimensional vector (x ir el, y ir el), representing the coordinate offset of the pixel relative to the upper left corner of the candidate region R t .

[0074] (3) Distance metric calculation: The distance metric function ρ is used to measure the feature of pixel i in the candidate region R t and the object feature p iThe differences between them. Taking the Bhattacharyya distance as an example, for the color histogram H i (the color histogram of the candidate region) and H t (the color histogram of the target region), the Bhattacharyya distance calculation formula is where K is the quantization interval number of the color histogram, and here K = 8×8×8). For the distance calculation of spatial information, the Euclidean distance can be used. For example, for the position coordinates (x irel , y irel )(the relative position coordinates of pixels within the candidate region) and ((x0, y0), the initial position coordinates of the target region), the spatial distance is Combining the color and spatial distances to obtain ρ([p i - p t ) 2 ).

[0075] (4) Weight calculation and optimization: Weights play a role in adjusting the contributions of different pixels to the target position estimation in the algorithm. Initially, the weights can be initialized according to the position distribution of pixels within the target region. Pixels in the central region have larger weights, while pixels in the edge region have smaller weights. Then, during the optimization process of the objective function, by minimizing the objective function J(t), the weights w i are continuously adjusted. In each iteration, according to the similarity between the current candidate region and the target region, the weights of pixels with high similarity are increased, and the weights of pixels with low similarity are decreased, making the estimation of the target position more accurate.

[0076] (5) Regularization processing: The regularization parameter λ is used to prevent overfitting and ensure the stability of the objective function during the optimization process. It constrains the norm ||w|| i of the weights w 2 to avoid the weights being too large, which may cause the model to be too complex and lose its generalization ability. The value of the regularization parameter λ is usually determined through experiments or experience. In the target tracking scenario of the gas detection operation, it may be adjusted according to the characteristics of the actual data and the tracking effect. In cases where the illumination changes greatly or the appearance of the target changes frequently, the value of λ is appropriately adjusted to balance the fitting ability and stability of the model.

[0077] (6) Target position update iteration: In each frame of the video image, by moving the candidate region within the search region (the size of the search region can be dynamically adjusted according to the target movement speed and scene complexity, and is initially set to 2 - 3 times the size of the target initial region R0), the objective function value J(t) of each candidate region is calculated. Then, the position of the candidate region that minimizes the objective function value is selected as the new position estimation R t =(x t , y t , w t , ht )。Next, according to the new target position R, update the target feature p t (including color histogram and spatial information), and repeat the above process, continuously iterating and optimizing the target position estimation until the objective function value converges or reaches the preset number of iterations.

[0078] 4. Output

[0079] (1) Target position update result: After the above complex calculation and iteration process, the updated position R of the target - the gas inspector or the gas detector in each frame is finally obtained t =(x t , y t , w t , h t ). This position information will be used as the key input for subsequent action recognition and verification steps to determine whether the gas inspector has performed actions such as holding the gas detector high, pinching the balloon with the hand, and lowering the head to read the data, and whether these actions are carried out within the specified area range. If it is specified that the gas detector should be located within a certain specific area when held high, the position of the gas detector obtained through tracking (x t , y t , w t , h t ) can be compared with this area range to determine whether the action is compliant.

[0080] (2) Ensuring continuous visual continuity: By repeating the above target tracking process in each frame of the video stream, the CSRT algorithm can maintain continuous visual continuity of the target. This means that the system can monitor the movement trajectory of the target in real time and accurately track the behavior changes of the gas inspector during the entire gas inspection operation. Whether the gas inspector is walking in the roadway, performing detection operations, or interacting with other equipment or personnel, the system can continuously track his position, providing a reliable basis for comprehensively evaluating the compliance of the gas inspection operation and ensuring that mistakes in judging the operation compliance will not occur due to interruptions or inaccuracies in target tracking.

[0081] 5. Action recognition and verification

[0082] When performing action recognition and verification, it is mainly divided into three steps: holding the gas detector high, pinching the balloon with the hand, and lowering the head to read the data. The specific steps are as follows.

[0083] (1) Holding the gas detector high: Among them, the system sets a specific methane detection area range as R=(x min , y min , x max , y max ). When the gas detector is held high to this range (x, y) and satisfies x min ≤x≤x max and y min ≤y≤ymax When this occurs, the system confirms the execution of the action through target tracking.

[0084] (2) Pinch the balloon: The system uses the target tracking algorithm to continuously monitor the hand movements of the gas inspector to ensure that the balloon is correctly and continuously pinched a specified number of times.

[0085] Let the initial position coordinates of the hand key points be and the position coordinates in the subsequent frames be By calculating the Euclidean distance When d t Within the preset pinching distance range (such as [d min , d max ) and the continuous satisfaction times reach the preset value N (such as 5 times), it is determined that the hand pinching balloon action is effective.

[0086] (3) Lower the head to read the value: When the operation of the gas detector enters the reading stage, the system confirms whether the reading step is correctly executed by tracking the head movements of the gas inspector.

[0087] Let the initial value of the head posture angle be α0, and the angle becomes α during the reading action t . By calculating the angle difference Δα = α t - α0, when Δα is within the preset reading angle range (such as [α min , α max ), it is determined that the action of lowering the head to read the value is effective.

[0088] 6. Judgment of non - compliance in gas inspection operations:

[0089] Formulate the judgment criteria for compliance in gas inspection operations. When the gas inspector's gas inspection operation does not conform to the criteria during the process, it is determined that the gas inspection operation is non - compliant. The specific criteria are as Figure 2 shown.

[0090] (1) Judge whether the gas inspector arrives on time

[0091] Analyze the arrival time T of the gas inspector through video images arrive and the specified arrival time T required . When T arrive > T required and the gas inspector does not carry a gas detector, it is determined that the gas inspection operation is non - compliant.

[0092] (2) Judge whether the actions of the gas inspector are compliant

[0093] Judge whether the actions of the methane detector meet the requirements through video images. The action process of a single methane concentration detection by the methane detector is divided into 3 steps: the methane detector holds the detector high, the methane detector pinches the balloon by hand, and the methane detector lowers the head to read the value. The methane detector needs to repeat the above action process three times. For the three action processes of a single methane concentration detection, it is determined as non-compliant when the following situations occur:

[0094] ① Holding the detector high: In a single detection process, if the number of times the detector fails to reach the methane detection area exceeds the threshold N1 (such as 1 time), this action is determined as non-compliant.

[0095] ② Pinching the balloon by hand: If the number of times of pinching the balloon does not reach the specified number N2, such as 5 times, or the action is not continuous, this action is determined as non-compliant.

[0096] ③ Lowering the head to read the value: If the action of lowering the head to read the value is not detected or the head posture angle is not within the specified range, this action is determined as non-compliant.

[0097] If the methane detector does not repeat the above action process three times, it is also determined as non-compliant.

[0098] This method also sets an alarm trigger mechanism: when the system detects the above non-compliant situations, such as the methane detector not operating according to the regulations, the operation is incomplete, or the recorded data is incorrect, the alarm is immediately triggered, and the on-site methane detector is informed to correct the action in the way of flashing lights, voice warning, and system notification. At the same time, the non-compliant information is sent to the relevant management personnel for processing.

[0099] The methane detection operation is carried out underground in the mine. The present invention first sets high-definition video cameras at key positions in the mine to ensure clear images and high recognition rates; then uses the YOLO algorithm of the neural network model to identify the methane detector + handheld methane detector to determine whether it arrives at the specified site on time; after confirming that it has arrived at the specified detection site, the CSRT target tracking algorithm is used to perform real-time tracking on the specified three actions, and compare with the standardized methane detection operation to determine whether it is executed and whether it is executed according to the number of times; after completing the three actions, fill in the methane detection information on the on-site record board. For non-compliant methane detection operations, a reminder is given in the form of an alarm to correct the non-compliant behavior. Through the above technical solutions, the problems of false detection, missed detection, and wrong detection existing in the methane detection operation can be effectively avoided, thus ensuring the compliance and reliability of the methane detection operation.

[0100] The embodiments of the present invention are described in detail above with reference to the drawings, but the present invention is not limited to this. All changes that can be made within the knowledge of those skilled in the art without departing from the purpose of the present invention are within the protection scope of the claims of the present invention.

Claims

1. A compliance recognition method for gas inspection operations based on a target tracking algorithm, characterized in that, It includes the following steps: Step S1: Arrange a high-definition video camera in the methane detection area of the mine, collect video data in the methane detection area in real time, and preprocess the images; Step S2: Use the YOLO object detection algorithm to identify the gas inspector and the gas detector in the methane detection area in the video; if it is detected that the gas inspector holds the gas detector and arrives at the designated methane detection area on time, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation; Step S3: After verifying that the gas inspector holds the gas detector and arrives at the designated methane detection area on time, use the CSRT object tracking algorithm to perform real-time tracking on the on-site gas inspection operation; then compare it with the set standardized operation. If the gas inspection operation conforms to the standardized operation, it is determined as a compliant operation, otherwise it is determined as a non-compliant operation; Among them, the standardized operation includes: (1) Single gas inspection action process: Hold the gas detector high, pinch the balloon by hand, and lower the head to read the value; (2) Repeat the single detection action process according to the set number of times.

2. The compliance recognition method for gas inspection operations based on the target tracking algorithm according to claim 1, wherein In step S1, the high-definition video camera has a wide dynamic range recognition function. Let the range of the illumination intensity in the mine be I min to I max , and the wide dynamic range DR of the selected camera satisfies 3. The method for identifying compliance of gas inspection operations based on the target tracking algorithm according to claim 1, characterized in that, In step S2, the method for determining whether the gas inspector arrives on time is to analyze the arrival time T of the gas inspector through video images arrive and the specified arrival time T required , when T arrive >T required , it is determined that the gas inspection operation is non-compliant.

4. The method for identifying compliance of gas inspection operations based on the target tracking algorithm according to claim 1, wherein In step S3, the method for comparing the on-site gas inspection operation with the standardized operation is: (1) Action determination of holding up the gas detector: Set the methane detection area range as R = (x min , y min , x max , y max ). When the gas detector is held up to this range (x, y) and satisfies x min ≤ x ≤ x max and y min ≤ y ≤ y max , confirm the execution of this action through target tracking; (2) Judgment of the action of squeezing the balloon: Use the target tracking algorithm to monitor the hand movement of the gas inspector. Let the initial position coordinates of the hand key points be the coordinates of the gas inspector's hand in the first frame, denoted as The position coordinates in subsequent frames are By calculating the Euclidean distance When d t Within the preset pinching distance range and when the continuous satisfaction times reach the preset value N, it is judged that the action of squeezing the balloon is effective; (3) Determination of the action of lowering the head to read: Assume that the initial value of the head posture angle is α0, and the angle becomes α during the reading action. t , by calculating the angle difference Δα = α t - α0, when Δα is within the preset reading angle range, it is determined that the action of lowering the head to read is effective.

5. The compliance recognition method for gas inspection operations based on the target tracking algorithm according to claim 1, characterized in that, In step S3, the single gas inspection action process is repeated 3 times.

6. The compliance identification method for gas inspection operations based on the target tracking algorithm according to claim 1, wherein, In step S3, the number of consecutive times of pinching the balloon by hand is 5-6 times.

7. The compliance identification method for gas inspection operations based on the target tracking algorithm according to claim 1, wherein Alarm for non-compliant operations in steps S2 and S3 to timely remind the monitoring personnel and the gas inspector.

8. The compliance recognition method for gas inspection operations based on the target tracking algorithm according to claim 7, wherein The said alarm includes visual signals, sound signals and electronic notifications.

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