PID-YOLO: Power line defect detection algorithm for drones

In this paper, we propose a novel deep learning-based power line defect detection algorithm, PID-YOLO, designed to significantly enhance the efficiency and accuracy of UAV inspections. This algorithm leverages an advanced deep learning network model that has been specifically improved to detect defects in small targets and complex backgrounds, a common challenge in the field of power line inspection. Through extensive training and testing using the InsPLAD dataset, we have demonstrated that PID-YOLO not only achieves an impressive average accuracy of 0.870 across multiple categories but also outperforms existing methods in terms of accuracy, recall, and real-time processing capabilities. This research delivers an efficient and accurate solution for the automatic detection of power line defects, which is particularly crucial for the construction of smart grids and the stable operation of power systems. The high average accuracy of 0.870 indicates that PID-YOLO is capable of identifying a wide range of defects with precision, while its superior recall and real-time processing power suggest that it can be effectively integrated into UAV inspection routines without compromising on speed or reliability. The implications of this research are profound, as it offers a cutting-edge solution that can potentially reduce the risk of power outages, improve the safety and efficiency of power line maintenance, and contribute to the advancement of smart grid technology. By automating the defect detection process with such a high degree of accuracy, PID-YOLO has the potential to revolutionize the way power line inspections are conducted, ultimately leading to more reliable and resilient power systems.