Journal of Applied Optics, Volume. 43, Issue 1, 87(2022)
Defects detection method of photovoltaic cells based on lightweightconvolutional neural network
[1] KABIR E, KUMAR P, KUMAR S, et al. Solar energy: potential and future prospects[J]. Renewable & Sustainable Energy Reviews, 82, 894-900(2018).
[2] DHIMISH M, HOLMES V, MEHRDADI B, et al. The impact of cracks on photovoltaic power performance[J]. Journal of Science:Advanced Materials and Devices, 2, 199-209(2017).
[8] LI J Y, SU Z F, GENG J H, et al. Real-time detection of steel strip surface defects based on improved YOLO detection network[J]. IFAC-PapersOnLine, 51, 76-81(2018).
[9] QIU Z, WANG S, ZENG Z, et al. Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach[J]. Journal of Electronic Imaging, 28, 43023-1-11(2019).
[11] ZHANG Y, SHEN Y L, ZHANG J. An improved tiny-yolov3 pedestrian detection algorithm[J]. Optik, 183, 17-23(2019).
[12] [12] JIANG B, LUO R, MAO J, et al. Acquisition of localization confidence f accurate object detection [EBOL]. [202107 20]. https:arxiv.gabs1807.11590.
[14] PARK J H, HWANG H W, MOON J H, et al. Automated identification of cephalometric landmarks: part 1—comparisons between the latest deep-learning methods YOLOV3 and SSD[J]. The Angle Orthodontist, 89, 903-909(2019).
Get Citation
Copy Citation Text
Huaiguang LIU, Wancheng DING, Qianwen HUANG. Defects detection method of photovoltaic cells based on lightweightconvolutional neural network[J]. Journal of Applied Optics, 2022, 43(1): 87
Category: OPTICAL METROLOGY AND MEASUREMENT
Received: Aug. 18, 2021
Accepted: --
Published Online: Mar. 7, 2022
The Author Email: Wancheng DING (dingwancheng_wust@163.com)