Surface Defect Detection of Solar Cells Based on Multiscale Region Proposal Fusion Network

Abstract

Manufacturing process and human operational errors may cause small-sized defects, such as cracks, over-welding, and black edges, on solar cell surfaces. These surface defects are subtle and, therefore, difficult to observe and detect. Accurate detection and replacement of defective battery modules is necessary to ensure the energy conversion efficiency of solar cells. To improve the adaptability to the scale changes of various types of surface defects of solar cells, this study proposed a multi-feature region proposal fusion network (MF-RPN) structure to detect surface defects. In such a network, region proposals are extracted from different feature layers of convolutional neural networks. Additionally, considering that multiple aspect ratios and scale settings and the use of multiple RPNs, result in an overlap of candidate regions and lead to information redundancy, we designed a multiscale region proposal selection strategy (MRPSS) to reduce the number of region proposals and improve network accuracy. Owing to the complete learning of shallow-detail texture information and deep semantic information, our multiscale RPN fusion structure can effectively improve an object’s multiscale feature extraction ability for various scales and types of surface defects of solar cells. Experimental results demonstrate that our method outperforms other methods by achieving a higher detection accuracy.

Keywords

• Deep learning
• defects detection
• faster R-CNN
• multiscale fusion
• RPN
• solar cell