Abstract
The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.
Highlights
The bird pest, lightning disturbance and external force damage are the three main obstacles of overhead transmission lines
Abnormal temperature rise (ATR) of composite insulator [5] and insulation breakdown are the main causes of electrical damage, and the activities of birds indirectly affect the insulation performance of transmission lines [6]
DETECTION NETWORK AND TRAINING In Faster_RCNN, the image is input into convolutional neural network for feature extraction, and regional proposal network (RPN) is used to generate proposals, which is mapped to the feature map of CNN’s last convolution layer
Summary
The bird pest, lightning disturbance and external force damage are the three main obstacles of overhead transmission lines. Girshick R et al proposed regional convolution neural network (RCNN) [16], [17], which used the selective search method to select several candidate regions of the same size in the image to be detected, and used the deep convolution neural network for high-level feature extraction.
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