Lightweight network for insulator fault detection based on improved YOLOv5

Abstract

Severe damage to insulators can hinder the daily operation of the power system. Current fault diagnosis methods heavily depend on manual visual inspection, leading to inefficiency and inaccuracies. While computer vision algorithms have been developed, their high requirements for running environments limit their applicability on edge devices. Additionally, the challenges in identifying insulator flashover faults have resulted in limited effectiveness in fault diagnosis. To address these issues, we introduce a novel one-stage network that enables real-time detection of insulator faults on mobile devices. We designed a new module that optimises the computational complexity of networks and fused the module with the attention mechanism SimAM to solve the problem of low efficiency in detecting flashover faults. Our research deploys multiple models on embedded devices in this article. Results indicate that the YOLOv5s-L-SimAM achieves the mAP of 93.9% and the model size is compressed to 9.4 MB, achieving the frame rate of 9.5 in the Jetson Xavier NX.

Keywords:

• Deep learning
• edge device
• insulator fault detection
• lightweight neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the scientific research project of Wenzhou under Grant ZF2022003.