The continuous expansion of the scale of transmission lines has increased the workload of identifying potential dangers around them. Because the inspection cycle of long-distance lines is too long, the potential dangers are not found in time, which often leads to the damage of power equipment, resulting in large-scale power outages and huge economic losses. Therefore, the topic of transmission line peripheral hidden trouble identification based on image edge detection and monocular vision is proposed. Firstly, transmission data is optimized based on image edge detection, image edge detection data is optimized, and data pretreatment is carried out on transmission data. The identification model of hidden dangers around transmission lines is designed and the identification results are obtained by calculating the model. Calculating the shortest distance between the transmission line and the hidden danger, less than 3 km, through the alarm to remind the staff. Finally, the relative errors of distance calculation results are controlled about 2% by an example.
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