Steel Surface Anomaly Detection Using 3D Depth and 2D RGB Features

Abstract: The detection of steel surface anomalies has become an industrial challenge due to different production equipment and processes, as well as differences in the characteristics of steel. To alleviate the problem, this paper proposes a detection and localization method combining 3D depth and 2D RGB features, which can be divided into three stages: defect classification, defect location, and warpage judgment. The first stage uses a data-efficient image transformer model, the second stage utilizes reverse knowledge distillation, and the third stage performs feature fusion using 3D depth and 2D RGB features. Experimental results show that the proposed algorithm achieve relatively high accuracy and feasibility, and can be effectively used in industrial scenarios.

Keywords: anomaly detection; anomaly localization; feature fusion; reverse distillation