SDDet: An Enhanced Encoder–Decoder Network With Hierarchical Supervision for Surface Defect Detection

Abstract

Surface defect detection (SDDet) is extremely critical for quality control and routine maintenance, and computer vision-based methods have delivered promising performance in various industrial fields. However, there remains big challenges in region consistency and boundary localization, due to complicated appearance in defects, interference of artifacts, and low contrast. To overcome these issues, in this article, we propose an enhanced encoder–decoder network with hierarchical supervision for SDDet. Specifically, deformable convolution is adopted to enable feature learning in a free-form way, thus making the network more robust to various defect structures. Then, a bridge module is applied to capture richer multilevel contextual information, which is conducive to distinguishing defects from the background artifacts. Next, the learned features from adjacent layers are adaptively combined by selective feature aggregation (SFA) module to enhance discriminative features and suppress valueless features. Meanwhile, the bottleneck refinement (BR) module is added to further refine the boundary of defect region, resulting in a satisfying boundary details. Finally, a hybrid three-level loss is developed to guide the network to focus more on error-prone hard pixels. Additionally, a hardware system embedded with our SDDet is built to achieve automatic and accurate defect inspection. Experimental results over four datasets demonstrate the effectiveness and generalization of our method compared with state-of-the-art methods.