Detection Technology of Intrusion Objects in Power Transmission Corridors Based on Convolutional Neural Networks

Abstract

Intrusion objects for power transmission corridors always present a big threat to the successful operation of the power grid. Due to the complexity and total length of the transmission corridors, the conventional field inspection methods for detecting these objects is a significant workload with low efficiency. This paper proposes the method for detecting transmission line intrusion objects, including engineering vehicles such as exactors and cranes. Object detection technology is used to locate and identify the engineering vehicles near power transmission lines. Experimental results show that, compared with traditional identification methods, the proposed method can provide improved accuracy with respect to the specific classification information of engineering vehicles and serve as an information basis for transmission line fault warning.