Lightweight insulator defect detection algorithm based on UAV perspective

As the primary method of low-altitude power monitoring, unmanned aerial vehicle (UAV) inspection presents the dual requirements of lightweight and high precision for the detection model. The timely detection of defects is crucial to the reliability of the power grid since it is the fundamental component of the safe operation of the power system. Based on YOLOv11, a lightweight insulator defect detection algorithm based on the UAV perspective is proposed. Firstly, in the YOLOv11 backbone network, the improved feature extraction unit of MobileNetV4, with a general inverted bottleneck structure, is integrated to enhance the perception of subtle defects of insulators. Secondly, the YOLOv11 neck network was integrated with a hierarchical spatial screening feature pyramid network, and the interaction path of cross-layer features was optimized to minimize model parameter redundancy. Finally, at the detection output, the dynamic deformable convolution detection head is used to replace the traditional detection module to improve the adaptability to the geometric deformation of defects. Experimental results show that compared with the YOLO series model, the proposed lightweight model can reduce the number of parameters by more than 12.35% on the basis of ensuring detection accuracy, which is more suitable for edge equipment such as UAVs and inspection robots, and provides an efficient solution for the real-time detection of transmission line insulator defects.