Pyramid cross attention network for pixel-wise surface defect detection

Abstract

Surface defect detection plays a crucial role in improving the overall quality of industrial production processes and ensuring that the resulting products meet the required quality standards. However, detecting these defects can be challenging due to various issues, including low image contrast, significant background noise, variable defect scales, and blurred boundaries. Although several segmentation networks have been developed to address these challenges, they still face difficulties in preserving fine-grained details during the encoding process and maintaining global features during the decoding process. Moreover, the simple skip connection that combines global and local information for feature fusion fails to consider their discrepancies and varying levels of significance. To overcome these limitations, this paper proposes a Pyramid Cross Attention Network (PCANet) for pixel-level surface defect detection. The encoder extracts multiresolution features, and the pyramid adaptive selection module (PASM) is introduced to supplement lost information and adaptively select information based on its importance. Furthermore, the cross attention fusion module (CAFM) is designed to address the incompatibility between the features extracted by the encoder and decoder in the raw skip connection, while strengthening defect areas and suppressing irrelevant noise using cross attention mechanisms. Finally, extensive experimental results demonstrate that the proposed PCANet outperforms other state-of-the-art methods in terms of mean Intersection over Union (mIoU). Specifically, it achieves an mIoU of 84.57 % in NEU-Seg and 79.37 % in MT_defect, respectively.