Journal of Optoelectronics · Laser, Volume. 33, Issue 5, 513(2022)
State detection of railway catenary insulators based on deep learning and gray-scale texture features
[5] [5] PING T,LI X F,XU J M,et al.Catenary insulator defect detection based on contour features and gray similarity matching[J].Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering),2020,21(1):64-73.
[6] [6] ZHANG G N,LIU Z G,HAN Y.Automatic recognition for catenary insulators of high-speed railway based on contourlet transform and Chan-Vese mode[J].Optik-International Journal for Light and Electron Optics,2016,127(1):215-221.
[8] [8] JI Y,ZHAO K,ZHANG K M,et al.An improved Faster R-CNN for devices detection inrailway 4C system[C]//38th China Control Conference,July 27-30,2019,Guangzhou,Guangdong,China.Beijing:Systems Engineering Society of China,2019:6.
[10] [10] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[11] [11] RUSSAKOVSKY O,DENG J,SU H,et al.Image net large scale visual recognition challenge International Journal of Computer Vision[J].2015,115(3):211-252.
[12] [12] MAKOTO S,KUMAR P R,YOSHIKI K,et al.Improved iterative reconstruction method for compton imaging using median filter[J].PloSone,2020,15(3):22-36.
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JIANG Xiangju, DU Xiaoliang. State detection of railway catenary insulators based on deep learning and gray-scale texture features[J]. Journal of Optoelectronics · Laser, 2022, 33(5): 513
Received: Sep. 3, 2021
Accepted: --
Published Online: Oct. 9, 2024
The Author Email: DU Xiaoliang (duxl2019@163.com)