Detection of Surface Defects in Solar Cells by Bidirectional-Path Feature Pyramid Group-Wise Attention Detector
Haiyong Chen, Mengyuan Song, Zezhi Zhang, Kun Liu
Abstract
Due to the multi-scale characteristics of defects and strong background interference, the automation of solar cell surface defect detection is still a challenge. To address this problem, this paper proposes a novel defect ohject detector called BPGA-Detector which consists of two parts: the Bidirectional-Path Feature Pyramid Network (BPFPN) and the Group-wise Attention Module (GAM). The Bidirectional-Path Feature Pyramid Network (BPFPN) combines multiscale features using a bidirectional-path feature fusion method that structured by connecting the bottom-up path feature pyramid network to the original FPN, preserving the characteristics of minor and weak flaws in the shallow layer. Furthermore, the Group-wise Attention Module (GAM) is elaborately designed to suppress the background disturbance and highlight the defect locations by connecting multi-layer contextuai features, which significantly improves the discriminant ability of small defects. Finally, the experimental results on a largescale solar cell dataset including 6263 images, 5763 of which are defective, demonstrate that the proposed method achieve superior detection performance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> up to 88.8%).