A horizontal telescopic disconnecting switch detection method, system and device based on semantic segmentation and a storage medium

Abstract

The application discloses a horizontal telescopic isolator detection method, system, device and storage medium based on semantic segmentation. The method extracts image frames from a video, pre-processes the images, trains a semantic segmentation model, and generates a model with the best mIou index. The isolator split and combined state images are used as a template base map, and split and combined polygon regions are labeled. The actual isolator state images are tested, and the test images are matched with the split and combined images to obtain the actual split and combined regions. The trained semantic segmentation model is loaded, the test images are subjected to knife arm mask acquisition, the obtained knife arm mask is subjected to contour acquisition, the contour is matched with the split and combined regions to obtain the actual state of the isolator. The application can improve the identification accuracy of the horizontal telescopic isolator of a transformer substation without adding additional sensors, and effectively solves the problem of remote identification of the isolator, especially in the case of isolator preset position deviation.

Description

Technical Field

[0001] This invention relates to the field of image semantic segmentation, and in particular to a method, system, device, and storage medium for detecting horizontally retractable disconnect switches based on semantic segmentation. Background Technology

[0002] Horizontal telescopic disconnectors achieve opening and closing operations through horizontal telescopic movement. They are widely used in 220kV and 500kV substations for power isolation, switching operations, and switching on and off low-current circuits. Their core structure includes: a conductive arm: employing a folding or telescopic design, forming a horizontal break when open to ensure a visible insulation gap; when closed, the conductive arm straightens and contacts the stationary contact, forming a conductive path; and an insulating support: typically a double-column structure, with one column supporting the stationary conductive contact and the other housing the folding movable arm. The insulating support serves both support and operational functions, ensuring insulation performance under high voltage.

[0003] The open / closed state of horizontal telescopic disconnectors directly affects the safety of power grid operation. If, during a load-bearing trip, the switch is mistakenly interpreted as open and operated accordingly, it may cause an arc short circuit, burning out equipment and even endangering personnel safety. Conversely, if the grounding switch is closed while the main disconnector is energized, closing it without confirming that the main disconnector is open can lead to a system grounding fault, causing a large-scale power outage. Before maintenance, it is essential to confirm that the disconnector is open and grounded; otherwise, maintenance personnel may come into contact with live parts, resulting in electric shock. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method for detecting horizontal telescopic disconnectors that can effectively achieve remote identification of disconnectors when the disconnector is offset without the need for additional sensors; another purpose of this invention is to provide a horizontal telescopic disconnector detection system, device and storage medium based on semantic segmentation.

[0005] Technical solution: The horizontally retractable disconnector detection method based on semantic segmentation of the present invention includes the following steps: Step S1: Construct a dataset of horizontal telescopic disconnect switches based on videos of horizontal telescopic disconnect switches in the substation; Step S2: Construct a semantic segmentation model, train it using the horizontally scalable disconnector dataset, and select the semantic segmentation model with the highest mIou index as the inference model; Step S3: Mark the polygonal regions of the disconnecting switch when it is open and when it is closed, respectively, to obtain the image template of the disconnecting switch's open / closed regions; Step S4: Perform affine transformations on the image templates of the disconnecting / closing areas of the disconnecting switch and the image to be detected to obtain the disconnecting / closing areas of the image to be identified; Step S5: Use the inference model to identify the image to be detected and calculate the knife arm mask; Step S6: Generate a contour based on the knife arm mask, calculate the intersection / union ratio with the split / combined region of the image to be identified, and determine the state of the horizontal telescopic disconnect switch by the intersection / union ratio.

[0006] Furthermore, step S1 includes the following steps: Step S11: Collect video of the horizontal telescopic disconnector in the substation. This video includes the process of the horizontal telescopic disconnector going from open to closed or from closed to open. Step S12: Extract keyframes from the acquired horizontal telescopic disconnector video using the OpenCV function library and save the images; Step S13: Perform data preprocessing on the saved image data. Data preprocessing includes: correcting images that are too dark or too bright, enhancing the details of the blade edge, scaling, adjusting contrast, and adjusting saturation. Step S14: Label the disconnector arms using the open-source software labelme to obtain the horizontal telescopic disconnector dataset.

[0007] Furthermore, step S2 includes the following steps: Step S21: Construct a semantic segmentation model based on the mmsegmentation framework and perform semantic segmentation training on the horizontally scalable disconnector switch. In the semantic segmentation model, the backbone network is selected as the Swin Transformer, and the pre-trained weights are selected as the swin_base_patch4_window12_384_22k model trained on ImageNet-22K. Step S22: In the DecodeHead stage, the Mask2Former algorithm is used. The window size and embedding dimension for self-attention calculation are set. The segmentation mask is generated through the Transformer decoder. The loss function is selected as classification loss, dice loss and mask loss. Step S23: During the training phase, the optimizer is AdamW, the learning rate, weight decay and momentum are set, the learning rate is adjusted, and the PolyLR strategy is adopted.

[0008] Furthermore, step S3 is specifically as follows: The open-source software Labelme was used to label the images of the disconnector switch when it is open and when it is closed. The label for the open area was named "open_area", and the label for the closed area was named "close_area". The coordinates of the open / closed areas were represented by the coordinates of the four vertices of a clockwise polygon. The coordinates of the closed area were... Regional coordinates .

[0009] Furthermore, step S4 includes the following steps: Step S41: Set the number of feature points to be detected, the detection threshold, and the radius threshold. Use the Superpoint network to extract key points and descriptors from the images of the opening / closing regions of the disconnector and the images to be detected, respectively. Step S42: Use the Lightglue network to perform local feature matching on the extracted keypoints, and output the index of the matched keypoints, the matching score, and the confidence level. Step S43: Calculate the fundamental matrix based on the key points of the output matching, and filter out mismatched points; wherein, the calculation of the fundamental matrix is ​​to set the maximum distance threshold from the point to the epipolar line, using the cv2.findFundamentalMat function of OpenCV, and the method parameter uses cv2.FM_RANSAC; Step S44: Calculate the homography matrix for the filtered matching points and set the reprojection error threshold; the homography matrix is ​​calculated using the cv2.findHomography function of OpenCV. Step S45: Perform a perspective transformation on the coordinates of the split / combined regions using OpenCV's cv2.perspectiveTransform function to obtain the transformed coordinates of the combined region. Transformed regional coordinates .

[0010] Furthermore, step S6 includes the following steps: Step S61: Preprocess the tool arm mask and extract its contour; Step S62: Use list S to store the extracted contour polygon information. 轮廓 express; Step S63: According to list S 轮廓 The combined coordinates of the transformed region S 合 and the transformed regional coordinates S 分 , please find Iou; Step S64: If Iou(S 轮廓， S 合 If Iou(S) > 0.9, then the task isolation switch is in the "closed" state; if Iou(S) > 0.9, then the task isolation switch is in the "closed" state; 轮廓， S 分 If the value is greater than 0.9, the task disconnect switch is in the "open" state; otherwise, the disconnect switch is in the "intermediate state".

[0011] Furthermore, the mask preprocessing involves using the function cv2.approxPolyDP to approximate the contours into polygons, filtering contours with an area less than the threshold of 30, smoothing the edges, and then extracting the contours.

[0012] The semantic segmentation-based horizontal telescopic disconnector detection system of the present invention includes: The dataset module is used to construct a dataset of horizontal telescopic disconnect switches based on videos of horizontal telescopic disconnect switches in substations. The model building module is used to build a semantic segmentation model. It is trained using a horizontally scalable disconnector dataset and the semantic segmentation model with the highest mIou index is selected as the inference model. The annotation and segmentation module is used to annotate the polygonal regions of the disconnector switch when it is open and when it is closed, respectively, to obtain images of the open / closed regions of the disconnector switch. The acquisition module is used to perform affine transformations on the images of the open / closed regions of the disconnector switch and the images to be detected, respectively, to obtain the open / closed regions of the image to be identified. The mask calculation module is used to identify the image to be detected using an inference model and calculate the knife arm mask. The calculation and judgment module is used to generate a contour based on the knife arm mask, calculate the intersection / union ratio with the split / combined region of the image to be identified, and determine the state of the horizontal telescopic disconnect switch by the intersection / union ratio.

[0013] The computer device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0014] The computer-readable storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0015] Beneficial effects: Compared with the prior art, the significant advantages of this invention are: This invention accurately determines the open / closed state of a horizontal telescopic disconnector switch through image semantic segmentation when only a complete open / closed video segment is acquired, even with minor changes in open / closed position; no manual intervention is required to adjust the pan / tilt position, as the algorithm automatically completes key point extraction, offset calculation, and image correction, ensuring stable and accurate determination of the disconnector switch's open / closed state even in complex field environments (such as old substations where pan / tilt offset is prone to occur); simultaneously, since no manual on-site camera adjustment or data re-acquisition is needed, the frequency and workload of manual inspections are significantly reduced, lowering the labor intensity and safety risks for maintenance personnel, and significantly improving the automation level and economic benefits of power equipment operation and maintenance. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] The method for detecting horizontally retractable disconnect switches based on semantic segmentation as described in this invention includes the following steps: Step S1: Construction of Horizontal Telescopic Disconnect Switch Dataset: Collect videos of horizontal telescopic disconnect switches in the substation, extract keyframes, and generate JSON files through data preprocessing and data augmentation. The specific steps are as follows: Step S11: Conduct video capture of the operation process of the horizontal retractable disconnector switch within the substation. Specifically, high-definition video data containing the complete operating cycle of the disconnector switch (i.e., from the open state to the closed state, or from the closed state to the open state) needs to be acquired. The captured video files should be stored in the widely compatible MP4 format to ensure the universality and ease of processing of the video data. Simultaneously, to ensure the clarity and detail of the video image, the video resolution is set to 1920 pixels wide × 1280 pixels high. Step S12: Based on the OpenCV open-source computer vision library, keyframe intelligent extraction processing is performed on the substation horizontal telescopic disconnector operation video acquired in Step S11. Specifically, an inter-frame difference algorithm combined with a timed sampling strategy is used, performing frame sequence filtering at 5-frame intervals—that is, after processing 5 consecutive video images, only the 5th frame is retained as the representative frame, and the remaining frames are automatically discarded. This sampling interval has been experimentally verified to reduce the amount of data to be labeled by 80% while ensuring the continuous monitoring of the action process, significantly reducing the workload of subsequent manual labeling.

[0018] Step S13: Perform multi-dimensional preprocessing on the JPEG format image data saved in step S12, specifically including: using an adaptive brightness enhancement algorithm based on Retinex theory to perform nonlinear stretching on overly dark areas and brightness compression to preserve local details in overexposed areas; and using the Canny edge detection operator combined with the Laplacian operator for sharpening to improve the gradient intensity of the blade edge. Step S14: Enhance the image using a combination of random scaling (uniform sampling within the range of 0.8×~1.2×), random rotation (-15°~+15° angle range), brightness adjustment (±30% dynamic range), contrast enhancement (γ value 0.7~1.3), and saturation change (±25%) to simulate the actual state of the disconnector switch under outdoor lighting conditions. Step S15: Use the open-source software labelme to annotate the enhanced disconnector image from Step S14. Annotate the main area of ​​the disconnector arm uniformly, excluding non-critical components such as the operating mechanism box and insulators. The label is “switch” and the annotation type is an irregular quadrilateral annotation.

[0019] Step S2: Model Design and Training: Design a semantic segmentation model, train it using the dataset from Step S1, and generate the inference model with the highest mIou. The specific steps are as follows: Step S21: The semantic segmentation model referred to in this step adopts a dedicated deep learning model built on the mmsegmentation open-source framework. This framework integrates a multi-scale feature fusion mechanism and dynamic computation graph optimization technology, and is specifically optimized for the semantic segmentation task of the horizontally telescopic disconnector switch area (pixel proportion of 8%~15%) in power equipment images. The model input layer is configured as a three-channel RGB image (resolution 384×384), and the output layer generates a single-channel probability map (continuous values ​​in the range of 0~1) with the same size as the input, which is then converted into a binary segmentation mask through thresholding (default threshold 0.7). Step S22: Select the Swin Transformer as the backbone network for feature extraction, with the following specific configuration: Window partitioning strategy: A 12×12 pixel non-overlapping sliding window mechanism is adopted, with the window displacement step size set to 1 / 2 of the window size (i.e., 6 pixels) to achieve cross-window information interaction; Embedding dimension control: After the input image is processed by a 4×4 pixel linear embedding layer, a 192-dimensional feature vector is generated; Pre-training transfer: The weights of the swin_base_patch4_window12_384_22k model trained on the ImageNet-22K dataset (14 million images, 22K categories) are loaded. These weights transfer high-level semantic features to the sub-domain of power equipment through knowledge distillation, which can significantly improve the initial convergence speed of the model; Step S23: In the DecodeHead stage, the Mask2Former (Mask-based Transformer) algorithm is used to achieve end-to-end segmentation. Its design includes: a Transformer decoder configured with 6 layers of cross-attention modules, each containing 12 attention heads (head dimension=64), enhancing spatial awareness through Dynamic Position Encoding (DPE); and the introduction of multi-task loss functions: classification loss (Focal Loss, γ=2.0) to address class imbalance; Dice loss (ε=1e-6) to optimize boundary region overlap; and mask loss (L1 Loss) to ensure segmentation continuity. The three loss functions are weighted and combined with a weight ratio of 0.4:0.3:0.3, resulting in an F1 score of 98.7% for the blade edge (width ≤ 5 pixels). Step S3: Generation of the split and merge regions: Use the open-source software Labelme to label and collect two images of the disconnector switch, one for splitting and one for merging. The split region is labeled "open_area," and the merge region is labeled "close_area." The coordinates of the split and merge regions are represented by the coordinates of the four vertices of a clockwise polygon. The coordinates of the merge region are... Regional coordinates ; Step S4: Perform affine transformations on the images to be detected using the images obtained from step S3, respectively, to determine the regions to be separated or merged in the image to be identified. The specific steps are as follows: Step S41: Using the Superpoint neural network architecture based on deep learning, feature point detection and descriptor extraction operations are performed on the standard image of the disconnector switch in the closed state and the image to be detected, respectively. Specific parameter configurations are as follows: Number of feature points to be detected: set to 4096 per image to ensure that the feature space distribution density meets the subsequent matching requirements; Detection confidence threshold: set to 0.05 to balance feature point detection sensitivity and noise suppression capability; Neighborhood radius threshold: set to 2 pixels for local feature point selection in non-maximum suppression processing. This step outputs a feature point set containing the spatial coordinates of the feature points and a 256-dimensional descriptor vector, providing basic data for subsequent feature matching.

[0020] Step S42: An improved Lightglue feature matching network is used to perform cross-domain matching on the feature point sets of the two images extracted in Step S41. This network enhances the correlation of local features through an attention mechanism, outputting a matching result set containing the following elements: (1) Matching point pair index: Records the index mapping relationship between the corresponding feature points in the standard image and the image to be inspected; (2) Matching similarity score: The quality assessment value of matching in the 0-1 interval calculated using cosine similarity; (3) Match confidence: The probability value of matching reliability in the 0-1 interval predicted by the neural network; (4) The matching results are sorted by confidence level, and the top 80% of high-confidence matching pairs are retained for subsequent processing.

[0021] Step S43: Use the cv2.findFundamentalMat function from the OpenCV library to perform the fundamental matrix calculation. The specific implementation parameters are as follows: Estimation method: The robust RANSAC (Random Sample Consensus) algorithm is selected; Distance threshold: Set the maximum distance for epipolar geometry constraints to 3 pixels; Number of iterations: The optimal number of iterations is automatically calculated based on the number of feature points; This step constructs epipolar geometric constraints using a fundamental matrix, eliminates mismatched point pairs that deviate from the constraints by more than a threshold, and outputs a geometrically validated set of high-quality matching points.

[0022] Step S44: Based on the matching point set filtered in step S43, the image homography transformation matrix is ​​calculated using OpenCV's cv2.findHomography function. Key parameter settings include: Reprojection error threshold: set to 2 pixels to ensure that the transformation accuracy meets the equipment detection requirements; Solution method: The Direct Linear Transform (DLT) algorithm is combined with RANSAC optimization. The calculated 3×3 homography matrix H is used to describe the projection transformation relationship between the standard image and the image to be inspected.

[0023] Step S45: Using the homography matrix H obtained in step S44, perform a perspective transformation on the coordinates of the key equipment area defined in step S32: Input coordinates: includes the set of vertex coordinates of the closing area S_close_in and the set of vertex coordinates of the opening area S_open_in; Transformation function: OpenCV's cv2.perspectiveTransform function is used; Output coordinates: Generates the transformed set of spatial coordinates; Closing area: S_close_out=[[x'1 close,y'1 close],[x'2 close,y'2 close],[x'3 close,y'3 close],[x'4 close,y'4 close]]; Opening area: S points_out=[[x'1 points, y'1 points], [x'2 points, y'2 points], [x'3 points, y'3 points], [x'4 points, y'4 points]]; The transformation results are used for subsequent device status identification and position accuracy verification to ensure that the coordinate mapping error is controlled within ±1.5 pixels.

[0024] Step S5: Use the model trained in step S2 to identify the image to be detected and obtain the knife arm mask; Step S6: Application of the disconnector detection system: Generate a contour based on the mask obtained in step S5, and calculate the intersection-exchange ratio with the separation and combination areas in step S4. Determine the actual state of the disconnector through the intersection-exchange ratio to realize the state detection of the horizontal telescopic disconnector. The specific steps are as follows: Step S61: Use the model trained in step S2 to infer and generate a knife arm mask from the image to be detected, preprocess the mask and extract its contours; Step S62: The mask preprocessing in step S61 mainly includes: using the function cv2.approxPolyDP to approximate the contour with polygons to reduce the number of contour points; filtering contours with an area less than the threshold of 30 to remove noise; and edge smoothing, first applying Gaussian blur to the binary mask and then extracting the contour to reduce jagged edges. Step S63: The contour polygon information generated in step S61 is represented by a list, S contour = [[x1,y1],[x2,y2]......[xn,yn]], where n represents the number of contour points; Step S64: Use the S-contour from step S63 and the S-combination and S-divide from step S45 to calculate Iou, where the formula for calculating Iou is as follows: ; Where A and B represent two regions (which can be bounding boxes, segmentation masks, contours, etc.), A represents the intersection area of ​​A and B (the area of ​​the overlapping part), and B represents the union area of ​​A and B (the total area covered by the two regions). In this example, A represents the S contour, and B represents the S union and S division.

[0025] Step S65: If Iou(Scontour, Sclose) > 0.9, the task isolation switch is in the "closed" state; if Iou(Scontour, Sclose) > 0.9, the task isolation switch is in the "closed" state; otherwise, the isolation switch is determined to be in the "intermediate state" (i.e., neither closed nor closed).

[0026] Step S66: Based on the Flask framework in Python, encapsulate the semantic segmentation model into an HTTP interface and design a horizontally scalable disconnector detection system. Receive JSON messages sent from the webpage via a POST interface. The image data of the disconnector switch to be tested is encoded in Base64; Step S67: The system receives Base64 encoded information, converts it into OpenCV NumPy, and finally outputs the status of the isolation switch and returns it to the front-end interface.

[0027] The semantic segmentation-based horizontal telescopic disconnector detection system of the present invention includes: The dataset module is used to construct a dataset of horizontal telescopic disconnect switches based on videos of horizontal telescopic disconnect switches in substations. The model building module is used to build a semantic segmentation model. It is trained using a horizontally scalable disconnector dataset and the semantic segmentation model with the highest mIou index is selected as the inference model. The annotation and segmentation module is used to annotate the polygonal regions of the disconnector switch when it is open and when it is closed, respectively, to obtain images of the open / closed regions of the disconnector switch. The acquisition module is used to perform affine transformations on the images of the open / closed regions of the disconnector switch and the images to be detected, respectively, to obtain the open / closed regions of the image to be identified. The mask calculation module is used to identify the image to be detected using an inference model and calculate the knife arm mask. The calculation and judgment module is used to generate a contour based on the knife arm mask, calculate the intersection / union ratio with the split / combined region of the image to be identified, and determine the state of the horizontal telescopic disconnect switch by the intersection / union ratio.

[0028] The computer device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0029] The computer-readable storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

Claims

1. A method for detecting horizontally retractable disconnect switches based on semantic segmentation, characterized in that, Includes the following steps: Step S1: Construct a dataset of horizontal telescopic disconnect switches based on videos of horizontal telescopic disconnect switches in the substation; Step S2: Construct a semantic segmentation model, train it using the horizontally scalable disconnector dataset, and select the semantic segmentation model with the highest mIou index as the inference model; Step S3: Mark the polygonal regions of the disconnecting switch when it is open and when it is closed, respectively, to obtain the image template of the disconnecting switch's open / closed regions; Step S4: Perform affine transformations on the image templates of the disconnecting / closing areas of the disconnecting switch and the image to be detected to obtain the disconnecting / closing areas of the image to be identified; Step S5: Use the inference model to identify the image to be detected and calculate the knife arm mask; Step S6: Generate a contour based on the knife arm mask, calculate the intersection / union ratio with the split / combined regions of the image to be identified, and determine the state of the horizontal telescopic disconnect switch by the intersection / union ratio; Step S2 includes the following steps: Step S21: Construct a semantic segmentation model based on the mmsegmentation framework and perform semantic segmentation training on the horizontally scalable disconnector switch. In the semantic segmentation model, the backbone network is selected as the Swin Transformer, and the pre-trained weights are selected as the swin_base_patch4_window12_384_22k model trained on ImageNet-22K. Step S22: In the DecodeHead stage, the Mask2Former algorithm is used. The window size and embedding dimension for self-attention calculation are set. The segmentation mask is generated through the Transformer decoder. The loss function is selected as classification loss, dice loss and mask loss. Step S23: During the training phase, the optimizer is AdamW, the learning rate, weight decay and momentum are set, the learning rate is adjusted, and the PolyLR strategy is adopted. Step S4 includes the following steps: Step S41: Set the number of feature points to be detected, the detection threshold, and the radius threshold. Use the Superpoint network to extract key points and descriptors from the images of the opening / closing regions of the disconnector and the images to be detected, respectively. Step S42: Use the Lightglue network to perform local feature matching on the extracted keypoints, and output the index of the matched keypoints, the matching score, and the confidence level. Step S43: Calculate the fundamental matrix based on the key points of the output matching, and filter out mismatched points; wherein, the calculation of the fundamental matrix is ​​to set the maximum distance threshold from the point to the epipolar line, using the cv2.findFundamentalMat function of OpenCV, and the method parameter is cv2.FM_RANSAC; Step S44: Calculate the homography matrix for the filtered matching points and set the reprojection error threshold; the homography matrix is ​​calculated using the cv2.findHomography function of OpenCV. Step S45: Perform a perspective transformation on the coordinates of the split / combined regions using OpenCV's cv2.perspectiveTransform function to obtain the transformed coordinates of the combined region S. 合 =[[x' 1合 ,y' 1合 ],[x' 2合 ,y' 2合 ],[x' 3合 ,y' 3合 ],[x' 4合 ,y' 4合 The transformed regional coordinates S 分 =[[x' 1分 ,y' 1分 ],[x' 2分 ,y' 2分 ],[x' 3分 ,y' 3分 ],[x' 4分 ,y' 4分 ]).

2. The method for detecting horizontally retractable disconnect switches based on semantic segmentation according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect video of the horizontal telescopic disconnector in the substation. This video includes the process of the horizontal telescopic disconnector going from open to closed or from closed to open. Step S12: Extract keyframes from the acquired horizontal telescopic disconnector video using the OpenCV function library and save the images; Step S13: Perform data preprocessing on the saved image data. Data preprocessing includes: correcting images that are too dark or too bright, enhancing the details of the blade edge, scaling, adjusting contrast, and adjusting saturation. Step S14: Label the disconnector arms using the open-source software labelme to obtain the horizontal telescopic disconnector dataset.

3. The method for detecting horizontally retractable disconnect switches based on semantic segmentation according to claim 1, characterized in that, Step S3 is as follows: The open-source software Labelme was used to label the images of the disconnector switch when it is open and when it is closed. The label for the open area was named "open_area", and the label for the closed area was named "close_area". The coordinates of the open / closed areas were represented by the coordinates of the four vertices of a clockwise polygon. 1合 ,y 1合 ], [x 2合 ,y 2合 ], [x 3合 ,y 3合 ], [x 4合 ,y 4合 ]] , regional coordinates [[x 1分 ,y 1分 ], [x 2分 ,y 2分 ], [x 3分 ,y 3分 ], [x 4分 ,y 4分 ]] .

4. The method for detecting horizontally retractable disconnect switches based on semantic segmentation according to claim 1, characterized in that, Step S6 includes the following steps: Step S61: Preprocess the tool arm mask and extract its contour; Step S62: Use list S to store the extracted contour polygon information. 轮廓 express; Step S63: According to list S 轮廓 The combined coordinates of the transformed region S 合 and the transformed regional coordinates S 分 , please find Iou; Step S64: If Iou(S 轮廓， S 合 If Iou(S) > 0.9, then the task isolation switch is in the closed state; if Iou(S) > 0.9, then the task isolation switch is in the closed state; 轮廓， S 分 If the value is greater than 0.9, the disconnect switch is in the open state; otherwise, the disconnect switch is in the intermediate state.

5. The method for detecting horizontally retractable disconnect switches based on semantic segmentation according to claim 4, characterized in that, The mask preprocessing involves using the function cv2.approxPolyDP to approximate the contours into polygons, filtering out contours with an area less than a threshold of 30, smoothing the edges, and then extracting the contours.

6. A horizontally telescopic disconnector detection system for implementing the semantic segmentation-based horizontally telescopic disconnector detection method according to any one of claims 1 to 5, characterized in that, include The dataset module is used to construct a dataset of horizontal telescopic disconnect switches based on videos of horizontal telescopic disconnect switches in substations. The model building module is used to build a semantic segmentation model. It is trained using a horizontally scalable disconnector dataset and the semantic segmentation model with the highest mIou index is selected as the inference model. The annotation and segmentation module is used to annotate the polygonal regions of the disconnector switch when it is open and when it is closed, respectively, to obtain images of the open / closed regions of the disconnector switch. The acquisition module is used to perform affine transformations on the images of the open / closed regions of the disconnector switch and the images to be detected, respectively, to obtain the open / closed regions of the image to be identified. The mask calculation module is used to identify the image to be detected using an inference model and calculate the knife arm mask. The calculation and judgment module is used to generate a contour based on the knife arm mask, calculate the intersection / union ratio with the split / combined region of the image to be identified, and determine the state of the horizontal telescopic disconnect switch by the intersection / union ratio.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images