DOI: 10.57760/sciencedb.10794
title: Metal Surface Defect Dataset
title: Metal Surface Defect Dataset

description: 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. Secondly, the MSDD is constructed using this technology. We achieve end-to-end surface defect detection using universal object detectors by mapping color space transformations to spatial domain transformations and employing hue randomization for data augmentation. Finally, four universal object detection methods, including FCOS, YOLOv5, YOLOv8, and RT-DETR are validated on this dataset. The results indicate that these models achieve an average precision of 85.4% on the dataset, significantly outperforming traditional methods. The MSDD consists of a total of 138,585 single-channel images and 9,239 mixed images, including 5746 defect-free images and 3493 images containing a total of eight types of defects. The defect patterns included are generally applicable for the automated visual inspection of casting-formed metal blank surface defects, highlighting its high research value.
description: 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. Secondly, the MSDD is constructed using this technology. We achieve end-to-end surface defect detection using universal object detectors by mapping color space transformations to spatial domain transformations and employing hue randomization for data augmentation. Finally, four universal object detection methods, including FCOS, YOLOv5, YOLOv8, and RT-DETR are validated on this dataset. The results indicate that these models achieve an average precision of 85.4% on the dataset, significantly outperforming traditional methods. The MSDD consists of a total of 138,585 single-channel images and 9,239 mixed images, including 5746 defect-free images and 3493 images containing a total of eight types of defects. The defect patterns included are generally applicable for the automated visual inspection of casting-formed metal blank surface defects, highlighting its high research value.