DSDNet: A Dual-Branch Network with Semantics and Details Aggregation for Defect Segmentation

Accurate defect segmentation is a tricky challenge due to large differences in defect sizes and appearances and blurred boundaries. In this paper, we propose a Dual-Branch Network with Semantics and Details Aggregation for Defect Segmentation, abbreviated as DSDNet. Specifically, DSDNet first applies the bilateral branches to encode semantic and detail information in defect images, respectively. In the semantic branch, we adopt the decoder network to reconstruct the encoder features layer by layer to mitigate the issue of feature distortion. To produce more accurate decoder features, we propose a Strip Context Fusion Module (SCFM) using various strip convolutions and channel attention through skip connections to fully extract context information. Simultaneously, we devise a Selective Semantics Aggregation Module (SSAM) via adaptively integrating multi-scale semantic information in the decoder stage with fine-grained boundary features to provide sufficient semantic guidance to the detail branch. Finally, to alleviate feature misalignment between the two branches, a Bilateral Attention Modulation Unit (BAMU) is introduced to facilitate the efficient feature fusion from semantic and detail branches through symmetric attention guidance. Experimental results on publicly available defect datasets, NEU-Seg, MT-Defect, and MSD, demonstrate that DSDNet can effectively improve segmentation accuracy compared with state-of-the-art algorithms.