Optimization of transmission line defect identification algorithm based on UAV images and application case study

In this study, the optimization and application case study of transmission line defect recognition algorithm based on UAV images are carried out. In response to the inefficiency, excessive costs and risky nature of conventional inspection approaches, an algorithm leveraging deep learning technology is presented for recognition purposes. The algorithm employs a CNN to extract features and classify, and is enhanced via data enhancement, feature extraction, classifier design and model training steps. The application case study shows that the algorithm achieves more than 90% accuracy in recognizing major defect types such as broken insulators, broken wires and tilted towers, which is highly adaptable and can effectively improve inspection efficiency and reduce costs. The research results show that the deep learning-based transmission line defect recognition algorithm has high accuracy and adaptability in practical applications, providing strong support for the safe and stable operation of the power system.