An insulator target detection algorithm based on improved YOLOv5

Abstract

Drone inspections are widely utilized in the detection of insulators in power lines. To address issues with traditional object detection algorithms, such as large parameter counts, low detection accuracy, and high miss rates, this paper proposes an insulator detection algorithm based on an improved YOLOv5 model. Firstly, in the backbone and neck networks, a lightweight CSP-SCConv module is employed to replace the original CSP-Darknet53 module, thereby reducing the parameter count and enhancing the feature extraction capabilities. Secondly, to broaden the image receptive field and improve feature fusion, an RFB model is incorporated into the neck network, replacing the original SPPF module. Additionally, a LSKBlock attention mechanism is appended at the end of the neck network to further obtain richer semantic information. Finally, to flexibly improve the accuracy of bounding boxes of different sizes and enhance the robustness of the model, an loss function is utilized to replace the original CIOU loss function. Experimental results demonstrate that the improved YOLOv5 model achieves a mean Average Precision (mAP) precision of 95.60%, with a parameter count of 18.36M and a computational load of 30.10G, respectively. The Precision (P) and Recall (R) are 88.10% and 95.20%, providing strong support for deployment on mobile devices for real-time detection.