Infrared and Laser Engineering, Volume. 49, Issue S2, 20200401(2020)
Insulator detection method based on feature selection YOLOv3 network
In order to solve the problem of insulator missing detection and inaccurate positioning caused by small proportion of insulators and complex background in infrared power image, a novel insulator detection network: Feature Selection YOLOv3(FS-YOLOv3) was proposed. The proposed FS-YOLOv3 added pyramid feature attention network to the top-down sampling process of the original pyramid shaped YOLOv3 network. The pyramid feature attention network calculated the feature weight matrix based on the high-level semantic feature map of YOLOv3, and used the feature weight matrix to filter out the redundancy of low-level detail features of the network. Finally, the low-level feature map and the high-level semantic feature map after feature filtering were connected in series to obtain the feature map with both accurate insulator detail information and rich high-level semantic information. The experimental results show that the detection accuracy of the proposed method is better than that of the original YOLOv3 network, and retains the good real-time characteristics of the original network.
Get Citation
Copy Citation Text
Chen Ming, Zhao Lianfei, Yuan Limin, Xu Feng, Han Mo. Insulator detection method based on feature selection YOLOv3 network[J]. Infrared and Laser Engineering, 2020, 49(S2): 20200401
Category: 图像处理
Received: Oct. 9, 2020
Accepted: Nov. 4, 2020
Published Online: Feb. 5, 2021
The Author Email: Ming Chen (chenming@jl.sgcc.com.cn)