A lightweight improvement of YOLOv5 for insulator fault detection

Zhou, Tianshui

doi:10.1088/1742-6596/2492/1/012029

Cited by 2 publications

(1 citation statement)

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“…Some scholars have proposed the insulator defect detection method [2] for multi-scale feature coding and dual attention fusion, whose accuracy has been significantly improved [20]. In addition, we also study and compare the YOLO series algorithms to detect transmission lines in real-time [27] and use embedded systems such as drones to carry the latest algorithms [28] to improve the algorithm's lightweight aspects, such as lightweight processing on the new network, CenterNet [29].…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Detection Algorithm Based on YOLOv5s for Power Line Self-Exploding Insulators

Hu,

Min,

Liu

et al. 2023

Electronics

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In the process of inspecting the self-exploding defects of power line insulators, traditional algorithms suffer from various issues such as long detection time, insufficient accuracy, and difficulties in effective detection under complex environments. To address these problems, we introduce an advanced one-stage object detection algorithm called YOLOv5s, which offers fast training and excellent detection performance. In this paper, we applied the YOLOv5s algorithm to improve the detection precision and classification accuracy of insulator self-explosions. To further enhance the YOLOv5s algorithm, we introduced a BiFPN (Bidirectional Feature Pyramid Network) module for feature fusion. This module improved the feature fusion process by learning the importance weights of different input features, considering their contributions. To tackle the challenge of detecting small objects in the self-exploding insulator dataset, we incorporated an SPD (spatial-to-depth convolution) module that focuses on capturing features in small regions and utilizes one-step convolution layers to avoid losing fine-grained information. To address the issue of high similarity between self-exploding insulator regions and intact insulator regions, we introduced an attention mechanism that concentrates attention on the defective insulator regions to gather more information about insulator defects. Experimental results validate that all three improvement methods significantly enhance detection precision. The final model achieves improvements of 2.0% in precision, 0.9% in recall, and 1.5% in average detection accuracy. Through target detection of the test dataset, insulators with self-explosion cases can be effectively detected.

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