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The multi-scale defect detection for photovoltaic (PV) cellelectroluminescence (EL) images is a challenging task, due to the featurevanishing as network deepens. To address this problem, an attention-basedtop-down and bottom-up architecture is developed to accomplish multi-scalefeature fusion. This architecture, called Bidirectional Attention FeaturePyramid Network (BAFPN), can make all layers of the pyramid share similarsemantic features. In BAFPN, cosine similarity is employed to measure theimportance of each pixel in the fused features. Furthermore, a novel objectdetector is proposed, called BAF-Detector, which embeds BAFPN into RegionProposal Network (RPN) in Faster RCNN+FPN. BAFPN improves the robustness of thenetwork to scales, thus the proposed detector achieves a good performance inmulti-scale defects detection task. Finally, the experimental results on alarge-scale EL dataset including 3629 images, 2129 of which are defective, showthat the proposed method achieves 98.70% (F-measure), 88.07% (mAP), and 73.29%(IoU) in terms of multi-scale defects classification and detection results inraw PV cell EL images.

BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection