Bird nest detection based on improved YOLO algorithm
Yang Bo1, Cao Xuehong1, Jiao Liangbao1, Kong Xiaohong2
1. Artificial Intelligence Industry Technology Research Institute, Nanjing Institute of Technology, Nanjing 211100; 2. State Grid Nanjing Power Supply Company, Nanjing 211100
Abstract：This paper proposes an object detection model based on YOLO deep learning algorithm, which is used for automatic detection of nests in electric drone inspection. Through the distance-based K-means clustering algorithm, the marker sets of the dataset are re-clustered. And the anchor set which is more suitable for identifying the nests in different environments of different towers is obtained. The test results show that the mean average precision (MAP) of the algorithm using the new set is increased to 0.896 while the recall rate and the average cross-over ratio are improved. The algorithm processing is close to real-time (less than 30ms), which can maintain the application requirements of intelligent and normalized power inspection.
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2020, Vol. 21 
