Metal Surface Defect Dataset

Automated Optical Inspection (AOI) technology plays a significant role in industrial defect detection.However, traditional static optical systems are affected by shadows and surface reflectivity, resulting in a high sensitivity to the direction of the illuminant, false positives, and missed detections, especially for metal parts with complex geometries. Moreover, there is still a lack of large-scale datasets for surface defect detection in such scenarios.

To address these issues, an automatic metal surface defect detection technique was proposed based on deep learning and photometric stereo vision, and a Metal Surface Defect Dataset (MSDD) was constructed. Firstly, a Stroboscopic Illuminant Image Acquisition (SIIA) method is proposed, which incorporates a specially designed arrangement of illuminants and a channel mixer to blend the collected multi-channel images into RGB pseudo-color images.