论文标题
BAF检测器：用于光伏细胞缺陷检测的有效基于CNN的检测器
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection
论文作者
论文摘要
光伏（PV）细胞电致发光（EL）图像的多尺度缺陷检测是一项具有挑战性的任务，因为随着网络的加深，特征消失了。为了解决这个问题，开发了基于注意力的自上而下和自下而上的体系结构，以完成多尺度功能融合。这种称为双向注意特征金字塔网络（BAFPN）的架构可以使金字塔的所有层具有相似的语义特征。在BAFPN中，使用余弦相似性来衡量融合功能中每个像素的重要性。此外，提出了一种新型的对象检测器，称为BAF-DETECTOR，该检测器将BAFPN嵌入更快的RCNN+FPN中。 BAFPN改善了网络对尺度的鲁棒性，因此所提出的检测器在多尺度缺陷中实现了良好的性能，可以检测任务。最后，大规模EL数据集的实验结果包括3629张图像，其中2129张有缺陷，表明所提出的方法可在多尺度的分类和检测结果中获得98.70％（f-measure），88.07％（MAP）和73.29％（IOU），而原始PV EL EL图像中的多尺度缺陷。
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.
