LFCA-YOLO: a lightweight insulator defect detection network based on local features and context attention

Abstract Detecting insulator defects is crucial for maintaining the reliability of power transmission and distribution systems. Addressing the challenges of high model complexity, low detection precision, and the difficulty of integrating with embedded devices, an LFCA-YOLO based on the local feature context attention enhancement mechanism is proposed, which effectively solves the problems of background noise interference and insufficient local feature capture by adding Concat_LFCA, C2f_LFCA, and PLFCA modules; the introduction of the ASCDown, CSPCLFCA, C2fCIB lightweight modules to improve the original performance of the model, while reducing the model parameters and computational complexity; Detect_SC through the design of shared convolution, balances the contradiction between the lightweight and accuracy of the detection head. Experiments on the IFD dataset show that the LFCA-YOLO model not only improves the AP 50 by 4.2%, but also decreases the number of model parameters and FLOPs, by 17% and 12.3% respectively compared with YOLOv8n. Additionally, the model’s ability to generalize has been confirmed through testing on both the Pascal VOC and SFID datasets. Notably, following the JOINT optimization process, the LFCA-YOLO model achieves an inference rate of 26 frames per second on the MAIX-III AXera-Pi, which satisfies the real-time detection demands for insulator defects. Our code and data are accessible via the provided GitHub .