Review of Deep Learning-Based Intelligent Inspection Research forTransmission Lines

Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence, robotics, and other related technologies. It plays a key role in ensuring power grid safety, reducing operation and maintenance costs, driving the digital transformation of the power industry, and facilitating the achievement of the dual-carbon goals. This review focuses on vision-based power line inspection, with deep learning as the core perspective to systematically analyze the latest research advancements in this field. Firstly, at the technical foundation level, it elaborates on deep learning algorithms for intelligent transmission line inspection based on image perception, covering object detection algorithms, semantic segmentation algorithms, and other relevant methodologies. Secondly, in application practice, it summarizes deep learning-based intelligent inspection applications across six dimensions—including detection of power insulators and their defects, transmission tower detection, power line feature extraction, metal fitting and defect detection, thermal fault diagnosis of power components, and safety hazard detection in power scenarios, and further lists relevant public datasets. Finally, in response to current challenges, it identifies five key future research directions, such as the deep integration of multiple learning paradigms, multi-modal data fusion, collaborative application of large and small models, cloud-edge-end collaborative integration, and multi-agent cluster control. This paper reviews and analyzes numerous deep learning-based intelligent detection methods for aerial images, comprehensively explores the application of deep learning in Unmanned Aerial Vehicle (UAV) inspection scenarios, and thus provides valuable theoretical and practical references for scholars engaged in smart grid automated inspection research.