http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Yang Tengfei (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Wang Shanshan,Tan Weiwei,Yang Tengfei,Zeng Liang,Hou Wenguang,Zhou Quan 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
With the outstanding performance of deep learning in the feld of computer vision, the automatic visual detection of foreign bodies in transmission lines and electrical equipment defects by inspection robots and Unmanned Aerial Vehicles based on neural networks has become an attractive topic in smart grid. However, in practical application scenarios, small-sized target defects pose a great challenge to the detection accuracy of existing mainstream deep learning detection networks with limited perceptual felds. To solve the above problems, the paper proposes a detection model of YOLOv5 transmission line inspection image. Firstly, the key target images under diferent backgrounds and attitudes are collected and preprocessed. Specifcally, in order to improve the perception ability of networks for small-sized targets, the K-means clustering algorithm is used to optimize the size of the anchor box, which efectively improves the ftting ability of the key target features. Then, recursive gated convolution is used as the backbone network to improve the ability to extract key target features. Finally, considering the concealment of small-scale features, the space-to-depth convolution module is added to the neck network to realize down-sampling and retain all the information in the channel dimension. In addition, a feature prediction layer is added to optimize the scale of network detection, and a Simple Parameter-Free Attention Module is added to further optimize the characterization of network features. The experimental results show that the accuracy and recall of the proposed network are 96.8% and 93.3%, respectively. The average detection accuracy reaches 97.1%, which is 3.8% higher than that of YOLOv5 network, 5.2% higher than YOLOv6 and 1.0% higher than that of YOLOv7 network. The proposed method signifcantly improves the detection performance of critical targets and defects of high-voltage transmission lines.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
