Electricity is an indispensable part in the development of national economy as well as people's life. Under the fast-paced social development trend, advanced intelligent equipment is widely used in the power system, and the manual power line inspection method is replaced by machines and equipment, which avoids all kinds of difficulties encountered by the staff in the inspection process. However, the power line inspection map captured by the intelligent power line inspection equipment and remotely sent to the dispatching terminal still requires staff to identify and analyze the specific power line inspection situation. In this paper, based on the image recognition technology of deep neural network, we analyze and select Faster R-CNN, a target recognition algorithm with high precision, and build a dedicated power line inspection image recognition model in TensorFlow deep learning platform. The model realizes automatic recognition of different types of power line inspection images transmitted back from remote locations, with high recognition accuracy, low misjudgment rate and omission rate, and can timely and accurately detect and identify hidden dangers in the process of power line inspection. In the power line inspection, the integration of image recognition technology, real-time effective identification of hidden dangers, and save the scheduling of manual work time, improve work efficiency.
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Li et al. (2024) studied this question.
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