UnfoldDet: Advancing Surface Defect Detection with Dual Feature Separation and Relation Reasoning

Surface Defect Detection (SDD) aims to accurately localize defects based on predefined category labels in industrial manufacturing. Different from generic object detection, the industrial environment introduces significant challenges due to interference and unrelated background textures, leading to increased confusion between defect and non-defect features. In this work, we identify and analyze the structural characteristics and relations inherent in defect features. This analysis enables effectively distinguishing defects from non-defect areas, thereby enhancing the discriminative power for surface defect detection. Based on this insight, we propose a novel surface defect detection framework, named UnfoldDet. This framework focuses on separating defect and non-defect features and reasoning about the relations among defects. Specifically, we formulate the feature separation as an optimization problem with structural constraints. By expressing its iterations as network stages, we introduce an unfolding fusion module (UFM) to progressively separate and fuse multi-scale features. At the instance level, we propose a hierarchical relation encoder (HRE) to capture the inherent relations among defect instances. Through reasoning on positional and categorical relations, only highly related defect features are enhanced, while unrelated non-defect features are suppressed. Through extensive quantitative and qualitative experiments, as well as ablation studies on real-world datasets including ESD, CSD, and NEU-DET, we demonstrate the effectiveness of the proposed UnfoldDet in terms of both performance and computational efficiency.