DPiT: Detecting Defects of Photovoltaic Solar Cells with Image Transformers

Solar energy is one of the most important resources that can be a clean and renewable alternative to traditional fuels. The collection process of solar energy mainly rely on the photovoltaic solar cells. The defects, such as microcracks and finger interruption on the photovoltaic solar cells can reduce its efficiency a lot. To solve this problem, defects detection of solar cells have attracted attention from many researchers. In this paper, we propose a novel transformer based network to detect defects on solar cells efficiently and effectively. First, we introduce convolutions into the transformer to enable positional information and spatial context more accurate and precise. Secondly, cross window based multi-head self-attention (CW-MSA) is proposed to enlarge the window relation modeling capacity. Finally, we propose a multi-scale aggregation block to merge the low-level features into deep semantically strong features by attention. Extensive experiments on the elpv dataset demonstrate DPiT can consistently bring significant improvements over its strong baseline Swin Transformer with subtle extra computational overhead. The visualization results show that the proposed DPiT is able to detect various complex defects correctly. In particular, DPiT can achieve impressive 91.7 top-1 accuracy and greatly outperforms other competitive counterparts.