IL-YOLO: An Efficient Detection Algorithm for Insulator Defects in Complex Backgrounds of Transmission Lines

Abstract

Insulators play a pivotal role in power transmission lines, and the timely detection of defects in insulators is crucial to prevent potentially catastrophic consequences in terms of human lives and property. This paper proposes an insulator defect detection algorithm, named Insulator Lack-You Only Look Once (IL-YOLO), addressing the limitations observed in existing research concerning the complex background and multi-target challenges in insulator detection. The IL-YOLO algorithm focuses on detecting insulator defects within the intricate background of power transmission lines. To enhance its functionality, we propose three improved modules. Firstly, the Insulator Lack-Global Attention Mechanism (IL-GAM) addresses issues such as the mutual influence of weights and loss of detailed information in the original module. Secondly, the Insulator Lack-C3 (IL-C3) module is designed to emphasize key information while preserving feature extraction and fusion. Lastly, the Insulator Lack-SPPFCSPC (IL-SPPFCSPC) module enhances attention to both key and global information while extracting effective information from multi-scale features. Experimental results demonstrate that IL-YOLO achieves a detection accuracy of 91.2%, marking a 3.6% improvement compared to the YOLOv5 algorithm. Furthermore, precision improves by 0.5%, recall increases by 6.3%, and the F1 score sees a boost of 3.8%. Notably, IL-YOLO achieves a frame rate of 90 frames per second (FPS), showcasing its capacity for real-time detection. Additional experiments affirm IL-YOLO's accuracy in completing insulator defect detection tasks in both general and complex backgrounds, highlighting its substantial advantages in addressing complex background and multi-target challenges.INDEX TERMS Complex background, insulator lack, multi-target, real-time detection, YOLO.

Insulator Defect Detection Based on YOLOv8s-SwinT

“…An adversarial reconstruction model, trained exclusively with standard samples, assesses defect states, achieving high localization accuracy. Zhang et al [25] introduced IL-YOLO, an insulator defect detection algorithm with three enhanced modules: IL-GAM, IL-C3, and IL-SPPFCSPC. Experimental results suggest that IL-YOLO offers notable advantages in handling complex backgrounds and multi-object challenges.…”