Oil and gas pipeline perimeter safety detection method based on deep learning

Abstract

The invention relates to an oil and gas pipeline perimeter safety detection method based on deep learning, and aims to improve the accuracy and efficiency of safety monitoring of a surrounding area of an oil and gas pipeline, and the method comprises the following steps: employing a high-definition camera to collect image and video data of the periphery of the oil and gas pipeline under different time and weather conditions, cleaning, preprocessing and labeling the data; then, an improved SSD algorithm is adopted to detect an engineering machinery target, the target recognition precision is improved through multi-scale feature fusion and a self-attention mechanism, and real-time target tracking is carried out in combination with Kalman filtering and a DeepSORT algorithm; and finally, carrying out time sequence analysis on the target movement track by using a long-short-term memory network, identifying abnormal behaviors, carrying out risk assessment, automatically triggering an early warning mechanism, and ensuring the safety of the pipeline. The method not only improves the accuracy of target detection and tracking, but also enhances the recognition capability of abnormal behaviors, and has high practical value.

Description

Technical Field

[0001] The present invention relates to the fields of deep learning, computer vision, safety detection and abnormal behavior analysis, and in particular to a method for safety detection of oil and gas pipeline perimeters based on deep learning. Background Art

[0002] In the security monitoring of modern oil and gas pipelines, especially in remote or remote areas, traditional manual inspections and static monitoring methods can no longer meet the growing security needs. Due to the long distance, complex geographical environment and potential external threats (such as illegal destruction, natural disasters, etc.), oil and gas pipelines require a more efficient, accurate and real-time security monitoring method. At present, although some automated detection technologies based on video surveillance have been proposed, most solutions have problems such as low detection accuracy, poor real-time performance, and inability to effectively identify complex behaviors.

[0003] Most existing security monitoring technologies rely on static images or traditional video monitoring methods, which are difficult to cope with multi-target detection and behavior analysis in complex dynamic environments. Especially in the perimeter security protection of oil and gas pipelines, how to accurately identify potential threats (such as illegal engineering machinery crossing the boundary, dangerous behaviors, etc.) and issue early warnings in a timely manner is still a technical problem that needs to be solved urgently. In addition, most existing target detection algorithms have poor adaptability to changes in the scale and shape of the target, lack sufficient flexibility and intelligence, and are prone to false alarms or missed alarms in complex pipeline environments.

[0004] Therefore, there is an urgent need for an intelligent safety detection method based on deep learning that can accurately identify engineering machinery targets in a dynamic monitoring environment and effectively perform behavior analysis and risk warning. This method must not only have high-precision target detection and tracking capabilities, but also identify potential safety hazards in advance through behavior analysis and risk assessment to effectively prevent oil and gas pipeline accidents. Summary of the invention

[0005] The purpose of the present invention is to provide a method for oil and gas pipeline perimeter safety detection based on deep learning, which can accurately and efficiently identify potential safety risks around oil and gas pipelines. This method improves the accuracy of target recognition by combining high-altitude monitoring, target detection, real-time tracking and behavior analysis technology, and can timely identify abnormal behaviors of potential threats such as construction machinery, accurately assess risks and automatically trigger early warning mechanisms, thereby effectively preventing safety accidents in oil and gas pipelines. In order to solve the problems of insufficient target recognition accuracy, difficult behavior analysis, and untimely risk warning in the prior art, the present invention proposes the following technical solutions: A method for oil and gas pipeline perimeter safety detection based on deep learning, characterized in that it includes the following steps: R1: Use high-altitude surveillance cameras to collect images and video data around the oil and gas pipeline, and clean, preprocess and annotate the collected raw data. The specific steps are as follows: R1-1 Install high-definition cameras and monitoring equipment around oil and gas pipelines to continuously monitor and collect images and video data at different times and under different weather conditions. The collected images and video data involve different types of construction machinery (such as excavators, bulldozers, cranes, etc.); R1-2 Clean the collected raw data, remove low-quality, duplicate or irrelevant data to ensure the high quality of the data set, use the median filter method to denoise the image, and perform contrast adjustment and brightness normalization to improve image quality; R1-3 Perform random rotation, mirror flipping and scale transformation on the image processed in step R1-2 to expand the data set; R1-4 uses manual annotation to accurately annotate the collected images and video data. The annotation content includes the location, type, movement trajectory and potential dangerous behaviors of the target (such as crossing the boundary, staying in the restricted area, etc.); R2: Use the preprocessed and labeled data in step R1 as input to perform target detection and tracking. The specific steps are as follows: R2-1 Take the data processed in step R1 as input, adopt the improved SSD algorithm, and dynamically adjust the anchor frame through the K-means clustering method based on the size characteristics of typical engineering machinery around the pipeline to adapt to the different scales and aspect ratios of engineering machinery, and enhance the accuracy and efficiency of target detection by optimizing the size and ratio of the anchor frame; R2-2 Based on step R2-1, an improved feature extraction module is designed, which combines the convolutional neural network with the region extraction network, adopts the multi-scale feature fusion method, that is, extracts features of different scales at different network levels, and further enhances the recognition ability of the target through the self-attention mechanism; R2-3 Based on the target detection in steps R2-1 and R2-2, the Kalman filter algorithm is used to track the detected engineering machinery target in real time, and the target's motion trajectory is obtained by combining the target bounding box information. The DeepSORT algorithm is further used to enhance the stability of target tracking, handle the problem of target occlusion and the distinction between similar targets, and obtain the target's speed, acceleration, motion direction and other information by analyzing the target's motion trajectory, providing a basis for subsequent behavior analysis; R3: Perform behavior analysis and anomaly detection on the engineering machinery targets detected and tracked in step R2, and conduct risk assessment and early warning based on their behavior patterns. The specific steps are as follows: R3-1 uses a long short-term memory network to perform time series analysis on the motion trajectory of the engineering machinery target and identify the target behavior pattern as normal or abnormal, where abnormal behavior patterns include crossing the boundary, approaching the pipeline, and disorderly parking; R3-2 Based on the target behavior pattern identified in step R3-1 and combined with the relative position of the target and the pipeline, the risk assessment model is used to evaluate the danger level of the target. When potential dangerous behaviors are identified, the system will automatically trigger the early warning mechanism according to the preset rules (such as crossing the boundary, approaching the pipeline), and send alarms to the monitoring personnel or the automated control system in real time. The early warning information includes text prompts, video tags, and even remote alarms directly through smart terminals.

[0006] The oil and gas pipeline perimeter safety detection method based on deep learning provided by the above technical solution has the following beneficial effects compared with the existing technology: Improve the accuracy of target detection and tracking: This method uses an improved SSD algorithm, combined with multi-scale feature fusion and self-attention mechanism, which can effectively improve the accuracy of target detection, especially in the recognition of different types of construction machinery in complex environments. By dynamically adjusting the size of the anchor frame to adapt to the scale changes of different targets, the accuracy and efficiency of detection are significantly improved, and it has better adaptability and accuracy than traditional methods.

[0007] Enhanced behavior analysis and anomaly detection capabilities: By introducing Kalman filtering and DeepSORT algorithms for target tracking and combining them with long short-term memory networks (LSTM) for behavior analysis, this method can accurately capture the target's motion trajectory and behavior patterns, and promptly identify potential abnormal behaviors (such as crossing the boundary, approaching pipelines, etc.). This abnormal behavior recognition based on time series analysis can effectively improve the early warning capabilities of potential risk behaviors, thereby reducing the possibility of accidents.

[0008] Improve the real-time and intelligent level of the system: The monitoring system of the present invention can analyze a large amount of video data in real time, automatically process target detection, behavior classification and risk assessment, without manual intervention, greatly improving the response speed and efficiency of the system. The automatic triggering of the early warning mechanism enables monitoring personnel to be informed of potential risks in the first place, further improving the safety of the perimeter of the oil and gas pipeline.

[0009] Adapt to the safety detection needs in complex environments: This method collects images through high-altitude monitoring cameras and combines deep learning technology to process target information in dynamic and complex scenes. It has strong environmental adaptability. Whether in bad weather, different lighting conditions, or in complex terrain, this system can work stably to ensure all-weather safety monitoring of the perimeter of oil and gas pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of the oil and gas pipeline perimeter safety detection method of the present invention; Figure 2 This is a scene diagram of the perimeter of an oil and gas pipeline in an embodiment of the present invention; Figure 3 An example diagram of the data annotation of the present invention; Figure 4 This is an example diagram of the test results of the present invention DETAILED DESCRIPTION

[0011] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific embodiments of the present invention, and does not strictly limit the scope of protection of the specific claims of the present invention.

[0012] This embodiment takes the vicinity of the Gegla oil pipeline in the Qinghai-Tibet Plateau as an example, and combines the Figure 1-4 , the present invention is further analyzed and explained.

[0013] The flow chart of the oil and gas pipeline perimeter safety detection method based on deep learning is as follows Figure 1 As shown, the following steps are included: Step 1: Use high-altitude surveillance cameras to collect image and video data around the Gegla oil pipeline on the Qinghai-Tibet Plateau, and clean, preprocess and annotate the collected raw data. The specific steps are as follows: Step 1.1: Figure 2 As shown, high-definition cameras and monitoring equipment are installed around the oil and gas pipelines to continuously monitor and collect images and video data at different times and under different weather conditions. The collected images and video data involve different types of construction machinery (such as excavators, bulldozers, cranes, etc.); Step 1.2: Clean the collected raw data, remove low-quality, duplicate or irrelevant data, ensure the high quality of the data set, use the median filter method to denoise the image, and perform contrast adjustment and brightness normalization to improve image quality; Step 1.3 Perform random rotation, mirror flipping and scale transformation on the image processed in step 1.2 to expand the data set, including: random rotation: rotating the image at different angles to generate multiple samples; mirror flipping: flipping the image horizontally or vertically to increase the diversity of the data; scale transformation: scaling the image to simulate targets of different sizes; Step 1.4: Figure 3As shown in the figure, the collected images and video data are accurately annotated by manual annotation. The annotation contents include: target position: annotate the position of each target in the image and use a bounding box to frame the target; target type: annotate different types of construction machinery, such as excavators, bulldozers, cranes, etc.; target motion trajectory: record the moving path of the target and annotate its direction and speed; potential dangerous behavior: annotate possible dangerous behaviors, such as the target crossing the boundary, staying in the restricted area, etc. Step 2: Use the preprocessed and labeled data in step 1 as input to perform target detection and tracking. The specific steps are as follows: Step 2.1 Take the data processed in step 1 as input, use the improved SSD algorithm, and dynamically adjust the anchor frame through the K-means clustering method based on the size characteristics of typical engineering machinery around the pipeline to adapt to the different scales and aspect ratios of engineering machinery, and enhance the accuracy and efficiency of target detection by optimizing the size and ratio of the anchor frame; Step 2.2 Based on step 2.1, an improved feature extraction module is designed, which combines the convolutional neural network with the region extraction network, adopts the multi-scale feature fusion method, that is, extracts features of different scales at different network levels, and further enhances the recognition ability of the target through the self-attention mechanism; Step 2.3 Based on the target detection in steps 2.1 and 2.2, the Kalman filter algorithm is used to track the detected engineering machinery target in real time, and the target's motion trajectory is obtained by combining the target bounding box information. The DeepSORT algorithm is further used to enhance the stability of target tracking and deal with the problem of target occlusion and the distinction between similar targets, such as Figure 4 As shown; by analyzing the target's motion trajectory, the target's speed, acceleration, motion direction and other information are obtained to provide a basis for subsequent behavior analysis; Step 3: Conduct behavior analysis and anomaly detection on the engineering machinery targets detected and tracked in step 2, and conduct risk assessment and early warning based on their behavior patterns. The specific steps are as follows: Step 3.1 Use the long short-term memory network to perform time series analysis on the motion trajectory of the construction machinery target and identify the target behavior pattern as normal or abnormal, where the abnormal behavior pattern includes crossing the boundary, approaching the pipeline, and disorderly parking; Step 3.2 Based on the target behavior pattern identified in step 3.1 and combined with the relative position of the target and the pipeline, the risk assessment model is used to assess the danger level of the target. When potential dangerous behaviors are identified, the system will automatically trigger the early warning mechanism according to the preset rules (such as crossing the boundary, approaching the pipeline), and send an alarm to the monitoring personnel or the automated control system in real time. The early warning information includes text prompts, video tags, and even remote alarms directly through smart terminals.

[0014] The present invention successfully realizes intelligent safety detection and risk warning of the perimeter of oil and gas pipelines by introducing deep learning algorithms, multi-scale feature fusion, real-time target tracking and behavior analysis technology. Through this method, the dynamic behavior of engineering machinery around the pipeline can be accurately identified, and combined with motion trajectory analysis and risk assessment, timely identification and warning of potential dangerous behaviors can be achieved. This method not only improves the accuracy of target detection and tracking, but also effectively prevents the occurrence of pipeline safety accidents, thereby greatly improving the efficiency and reliability of oil and gas pipeline safety monitoring.

[0015] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for oil and gas pipeline perimeter safety detection based on deep learning, characterized in that: The following steps are involved: R1: Use high-altitude surveillance cameras to collect images and video data around the oil and gas pipeline, and clean, preprocess and annotate the collected raw data. The specific steps are as follows: R1-1 Install high-definition cameras and monitoring equipment around oil and gas pipelines to continuously monitor and collect images and video data at different times and under different weather conditions. The collected images and video data involve different types of construction machinery (such as excavators, bulldozers, cranes, etc.); R1-2 Clean the collected raw data, remove low-quality, duplicate or irrelevant data to ensure the high quality of the data set, use the median filter method to denoise the image, and perform contrast adjustment and brightness normalization to improve image quality; R1-3 Perform random rotation, mirror flipping and scale transformation on the image processed in step R1-2 to expand the data set; R1-4 uses manual annotation to accurately annotate the collected images and video data. The annotation content includes the location, type, movement trajectory and potential dangerous behaviors of the target (such as crossing the boundary, staying in the restricted area, etc.); R2: Use the preprocessed and labeled data in step R1 as input to perform target detection and tracking. The specific steps are as follows: R2-1 Take the data processed in step R1 as input, adopt the improved SSD algorithm, and dynamically adjust the anchor frame through the K-means clustering method based on the size characteristics of typical engineering machinery around the pipeline to adapt to the different scales and aspect ratios of engineering machinery, and enhance the accuracy and efficiency of target detection by optimizing the size and ratio of the anchor frame; R2-2 Based on step R2-1, an improved feature extraction module is designed, which combines the convolutional neural network with the region extraction network, adopts the multi-scale feature fusion method, that is, extracts features of different scales at different network levels, and further enhances the recognition ability of the target through the self-attention mechanism; R2-3 Based on the target detection in steps R2-1 and R2-2, the Kalman filter algorithm is used to track the detected engineering machinery target in real time, and the target's motion trajectory is obtained by combining the target bounding box information. The DeepSORT algorithm is further used to enhance the stability of target tracking, handle the problem of target occlusion and the distinction between similar targets, and obtain the target's speed, acceleration, motion direction and other information by analyzing the target's motion trajectory, providing a basis for subsequent behavior analysis; R3: Perform behavior analysis and anomaly detection on the engineering machinery targets detected and tracked in step R2, and conduct risk assessment and early warning based on their behavior patterns. The specific steps are as follows: R3-1 uses a long short-term memory network to perform time series analysis on the motion trajectory of the engineering machinery target and identify the target behavior pattern as normal or abnormal, where abnormal behavior patterns include crossing the boundary, approaching the pipeline, and disorderly parking; R3-2 Based on the target behavior pattern identified in step R3-1 and combined with the relative position of the target and the pipeline, the risk assessment model is used to evaluate the danger level of the target. When potential dangerous behaviors are identified, the system will automatically trigger the early warning mechanism according to the preset rules (such as crossing the boundary, approaching the pipeline), and send alarms to the monitoring personnel or the automated control system in real time. The early warning information includes text prompts, video tags, and even remote alarms directly through smart terminals.

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