Deep Learning based Defect Detection Algorithm for Solar Panels

Jiaqi Li1; Hongxu Li1; Yifan Wu2; Hailiang Zhou3; Luigi Manfredi4; Peng Li1; Hong Zhang5

10.1109/WRCSARA60131.2023.10261859

2023

2835-3366

979-8-3503-0733-7

2023 WRC Symposium on Advanced Robotics and Automation (WRC SARA)

438-443

19-19 Aug. 2023

Beijing, China

Defect detection of solar panels plays an essential role in guaranteeing product quality within automated production lines. However, traditional manual inspection of solar panel defects suffers from low efficiency. This paper proposes an enhanced YOLOv5 algorithm (EL-YOLOv5) fused with the CBAM hybrid attention module to ensure product quality. The algorithm focuses on detecting five common types of defects that frequently appear on photovoltaic production lines, namely hidden cracks, scratches, broken grids, black spots, and short circuits. This study utilizes publicly available solar panel datasets, as well as datasets collected from actual photovoltaic production lines. These datasets are annotated accordingly and used to train the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves good performance on both the public and actual solar panel defect datasets. Particularly in actual datasets, where defect features are often less apparent and defects are smaller in size, the proposed algorithm can still detect even minor black spots.

Training Production Manuals Inspection Feature extraction Product design Quality assessment

其他

EI

20234214914908

Deep learning ; Defects ; Quality control ; Signal detection ; Solar concentrators ; Solar power generation

Ergonomics and Human Factors Engineering:461.4 ; Solar Power:615.2 ; Solar Energy and Phenomena:657.1 ; Solar Cells:702.3 ; Information Theory and Signal Processing:716.1 ; Quality Assurance and Control:913.3 ; Materials Science:951

IEEE

1.Department of Key Lab of Industrial Computer Control Engineering, Hebei Province Yanshan University, Qinhuangdao, China
2.Department of Computing Science, University of Alberta, Edmonton, AB, Canada
3.Hebei Baoding Jiasheng Photovoltaic Technology Co., Ltd.
4.Department of Institute for Medical Science and Technology (IMSaT), University of Dundee, United Kingdom
5.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Jiaqi Li,Hongxu Li,Yifan Wu,et al. Deep Learning based Defect Detection Algorithm for Solar Panels[C],2023:438-443.