Power transmission line insulator defect detection method based on deep learning

By introducing spatial object attention modules and extrusion excitation networks into the YOLO framework, adding small object detection layers, and adopting anchor frame detection and dynamic label allocation strategies, the problems of high error detection and high leakage detection rates in insulator defect detection of transmission line are solved, achieving higher detection accuracy and efficiency.

CN119672307APending Publication Date: 2025-03-21KUNMING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411749856.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

The existing deep learning-based insulator defect detection methods for transmission line insulators have problems with high error detection and high leakage detection rates in complex backgrounds and small target detection, which is difficult to meet the needs of power inspection.

Method used

The YOLO framework is improved by introducing the spatial object attention module (RCS-OSA) and the extrusion excitation network (SENet), a small object detection layer is added, and an anchor-free frame detection and dynamic label allocation strategy is adopted to optimize the detection head structure.

Benefits of technology

It significantly improves the comprehensive performance of insulator defect detection, improves the detection accuracy of small target defects and complex backgrounds, reduces the false detection and leakage detection rate, and is suitable for multi-scenario transmission line insulator defect detection.

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Abstract

The invention discloses a power transmission line insulator defect detection method based on deep learning, and the method comprises the steps: S1, obtaining an aerial image, carrying out the preprocessing of the aerial image, and generating an insulator defect detection data set; s2, based on a YOLOv8n model, introducing a space object attention module (RCS-OSA), and improving the feature extraction capability under a complex background; s3, adding an extrusion excitation network (SENet) in the neck network of the model to enhance the feature expression ability between channels; s4, constructing a small target detection layer, and adding a high-resolution feature map (160 * 160) to capture small defect target information; s5, optimizing a detection head by using multi-scale feature fusion and a dynamic label distribution strategy, and improving the recognition precision of the model on insulator defects under a complex background; and S6, outputting defect positions, types and confidence coefficients based on a detection result, and generating an electric power inspection report. According to the method, the accuracy and robustness of insulator defect detection under the complex background of the power transmission line are effectively improved, and the method has relatively high applicability and real-time performance.
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