IDD-YOLOv5: A Lightweight Insulator Defect Real-time Detection Algorithm

In order to achieve real-time detection of insulator defects in transmission lines, this paper proposes an improved insulator defect detection algorithm based on the YOLOv5s model, named IDD-YOLOv5. Firstly, to enhance the detection speed and reduce the model size, we introduce the GhostConv module and C3Ghost module into the backbone network. Secondly, the neck network employs a combination of C3Ghost and GSConv to further reduce the model’s parameter count, combined with the adaptive upsampling operator CARAFE to improve model accuracy. Finally, we adopt the EIOU loss function to accelerate model convergence and enhance detection precision. Experimental results show that IDD-YOLOv5 improves the mAP@0.5 by 2.5% over the original YOLOv5s, with accuracy and recall also increasing by 1.9% and 2.5%, respectively, while reducing parameters by 45.54%. Therefore, this improved model can rapidly and accurately detect insulator defects, aiding in the stable operation of electrical equipment. The related code, dataset, and model can be accessed through the following link: https://github.com/LuYang-2023/ICMA2024.git.