Insulator defect detection algorithm based on adaptive feature fusion and lightweight YOLOv5s

• Published in: Journal of real-time image processing Vol. 22; no. 1; p. 12
• Main Authors: He, Zhendong, Wang, Yiming, Zheng, Anping, Liu, Jie, Lou, Taishan, Zhang, Jie, Jiang, Penghao, Xu, Jiong
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025; Springer Nature B.V
• Subjects: Accuracy; Adaptive algorithms; Adaptive sampling; Algorithms; Artificial neural networks; Blackouts; Computer Graphics; Computer Science; Damage detection; Deep learning; Defects; Efficiency; Feature maps; Image contrast; Image enhancement; Image manipulation; Image Processing and Computer Vision; Machine learning; Multimedia Information Systems; Neural networks; Object recognition; Pattern Recognition; Power lines; Signal,Image and Speech Processing; Target detection; EfficientNet; Insulator recognition; ASFF; YOLOv5s
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-024-01589-4

In power line inspections, detecting insulator defects is critical due to the potential breakdown and damage resulting from long-term exposure to the natural environment. However, challenges persist in practical detection processes, such as the limited availability of insulator defect images, difficulty detecting minor defects, and high memory consumption of deep learning models. We propose an insulator defect detection algorithm based on adaptive feature fusion and the lightweight YOLOv5s model to address these issues. First, we enhance the existing dataset by employing techniques like flipping, random pixel manipulation, noise addition, and contrast/brightness adjustments to increase the diversity of training samples. Next, we integrate the adaptive feature fusion (ASFF) module to enable the network to learn relationships between different feature maps, enhancing semantic information and improving the network's ability to detect minor defects. Finally, we replace the backbone of YOLOv5s with the lightweight convolutional neural network EfficientNet, making the network more efficient and enabling it to focus more on target features. Experimental results demonstrate significant improvements in insulator defect detection. The model achieves an accuracy rate of 96.4%, a recall rate of 96.1%, and an mAP of 97.6%, effectively enhancing network performance.