  The multi-scale defect detection for photovoltaic (PV) cell
electroluminescence (EL) images is a challenging task, due to the feature
vanishing as network deepens. To address this problem, an attention-based
top-down and bottom-up architecture is developed to accomplish multi-scale
feature fusion. This architecture, called Bidirectional Attention Feature
Pyramid Network (BAFPN), can make all layers of the pyramid share similar
semantic features. In BAFPN, cosine similarity is employed to measure the
importance of each pixel in the fused features. Furthermore, a novel object
detector is proposed, called BAF-Detector, which embeds BAFPN into Region
Proposal Network (RPN) in Faster RCNN+FPN. BAFPN improves the robustness of the
network to scales, thus the proposed detector achieves a good performance in
multi-scale defects detection task. Finally, the experimental results on a
large-scale EL dataset including 3629 images, 2129 of which are defective, show
that 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 in
raw PV cell EL images.
