Surface defect detection and semantic segmentation with a novel lightweight deep neural network

Abstract

Abstract Current approaches to defect detection and segmentation make essential use of machine learning methods. To develop lightweight models is one of key tasks for many defect detection and segmentation applications. In this work, we present a lightweight trilateral parallel feature extraction with multi-feature aggregation network (TriMFANet) for surface defect detection and segmentation. In TriMFANet, the top lateral is the feature-rich extraction used to capture detailed information. The other two laterals, efficient semantic feature extraction (ESFE) and reverse ESFE, leverage Hadamard product attention to jointly extract deep-level global feature information. Additionally, the MFA module employs origin-symmetric sigmoid attention to enhance deep feature information and integrates the triple features. We conducted binary defect segmentation tasks on the SD-saliency-900 and RSDDs datasets, achieving outstanding performance in both S α and E ξ . For multi-class defect detection tasks on the NEU-Seg and MSD datasets, we rank first with mIoU scores of 79.0% and 81.2% respectively. Experimental results demonstrate that our lightweight model with only 90 K parameters exhibits excellent performance.