RERN: Rich Edge Features Refinement Detection Network for Polycrystalline Solar Cell Defect Segmentation

Abstract

High-performance defect segmentation techniques are essential for the high-quality manufacturing of polycrystalline solar cells. Edge detection is an effective technique to accurately locate the edge of defects. However, the existing methods ignore global channel information and the representation gap between multiscale features, inhibiting the ability of the network to aggregate discriminative features. In this paper, we propose a novel Rich Edge Features Refinement Detection Network (RERN) consisting of an encoder-decoder structure that captures rich discriminative edge feature representations by interactively exploring global spatial and channel context. To refine the dense global contextual information in the decoding layer, we propose a Bi-Directional Strip Refinement Attention (BSRA) adaptively capturing long-range spatial dependency with direction information and long-range channel dependency. BSRA is a lightweight and general module that can be easily inserted into the existing edge detection networks with a negligible computational burden. In addition, we release a polycrystalline solar cell defect edge (PSCDE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{1}$</tex-math></inline-formula> ) dataset that is the first high-quality segmentation database to advance the development of high-quality polycrystalline solar cell manufacturing. Our method achieves an ODS F-measure of 0.854 on the PSCDE dataset with strict evaluation criteria (maxDist=0.0015), outperforming existing state-of-the-art methods. To further verify the generalization ability of BSRA, we apply BSRA to other edge detection networks, and experiments show that the module further improves the accuracy of these methods on PSCDE.