Bird's Nest Detection Method on Electricity Transmission Line Tower Based on Deeply Convolutional Neural Networks

Birds nesting on electricity transmission line towers have potential hazards to the whole electricity transmission system. At present, it mainly relies on manual inspection to determine whether bird's nests exist, which not only has heavy workload, high missed detection rate, but also low efficiency. Therefore, in order to ensure the safe and reliable operation of the power grid system, eliminate hidden dangers in time, and reduce the adverse effects of bird activities on electricity transmission line towers, it is necessary to monitor and warn the nesting behavior in electricity transmission line towers. Therefore, in order to improve the efficiency and accuracy of bird's nest detection, a detection system for bird's nest is designed based onconvolutional neural networks technology taking common bird's nest pictures as samples and adopting CNN network structure. The comparative experiments proved that the model can effectively improve the identification accuracy of bird's nest on the electricity transmission line tower.