SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

Any defects or damage on photovoltaic panels significantly reduce photoelectric conversion efficiency and service life. Therefore, the refined defect segmentation technology is the key to the quality inspection of photovoltaic equipment. However, the existing methods ignore the information interaction between multi-levels, causing semantic features to be easily lost and difficult to fuse. In this paper, we propose a segmentation network based on an interactive fusion of semantic information for solar cell defect identification, which can effectively interact with multi-level rich information and enhance the positioning capabilities of high-level semantics. Specifically, we propose a novel Multi-Level Pyramid Strategy (MLPS) to reduce the information gap between multi-level features. MLPS adopts a pooling distribution mechanism to enable multi-level features to interact effectively in the feature pyramid. In addition, to effectively integrate high-level positioning semantics into the neighbor level, we design a new Neighbor Attention Fusion module (NAF) to propagate high-level semantic information gradually. NAF learns spatial neighbor-level similarity combined with high-level attention to balance location information and contextual features. We perform comprehensive experiments to demonstrate the advancement of our approach. Our method achieves 84.3% MIOU on our solar cell dataset, outperforming existing state-of-the-art (SOTA) methods. To verify the generalization ability of our network, we train and evaluate the PSCDE dataset. The results show that our model achieves an excellent 98.35%MIOU.