改进实时目标检测算法的电力巡检鸟巢检测

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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.

Key words： bird’s nest detection power inspection deep learning object detection you only look once (YOLO)

Received: 05 December 2019

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	Articles by authors
	Yang Bo
	Cao Xuehong
	Jiao Liangbao
	Kong Xiaohong

Cite this article:

Yang Bo,Cao Xuehong,Jiao Liangbao等. Bird nest detection based on improved YOLO algorithm[J]. Electrical Engineering, 2020, 21(5): 21-27.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I5/21

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