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. 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.

自动光学检测（Automated Optical Inspection, AOI）技术在工业缺陷检测领域发挥着重要作用。然而传统静态光学系统易受阴影与表面反射率影响，对光源方向敏感度高，易产生误检与漏检问题，针对复杂几何形状的金属部件尤为突出。此外，此类场景下的表面缺陷检测仍缺乏大规模专用数据集。为解决上述问题，本文提出了一种基于深度学习与光度立体视觉的金属表面缺陷自动检测技术，并构建了金属表面缺陷数据集（Metal Surface Defect Dataset, MSDD）。首先，本文提出了频闪光源图像采集（Stroboscopic Illuminant Image Acquisition, SIIA）方法，该方法采用特殊设计的光源布局与通道混合器，将采集到的多通道图像融合为RGB伪彩色图像。其次，依托该技术构建了MSDD数据集。随后，通过将色彩空间变换映射至空间域变换，并采用色调随机化策略开展数据增强，本文利用通用目标检测器实现了端到端的表面缺陷检测。最后，本文在该数据集上验证了FCOS、YOLOv5、YOLOv8以及RT-DETR四种通用目标检测方法的性能。实验结果表明，上述模型在该数据集上的平均精度可达85.4%，显著优于传统检测方法。MSDD数据集共包含138585张单通道图像与9239张混合图像，其中无缺陷图像5746张，含缺陷图像3493张，涵盖共计8类缺陷。所涵盖的缺陷类型普遍适用于铸造金属毛坯表面缺陷的自动化视觉检测，凸显了该数据集的高科研价值。

搜集汇总

背景与挑战

背景概述

该数据集是针对金属表面缺陷检测构建的大规模数据集，旨在解决传统光学检测中因阴影和反射导致的误报和漏检问题。它采用基于光度立体视觉的创新图像采集技术，包含超过14万张图像，涵盖八种常见缺陷类型，并已验证多种深度学习模型，平均检测精度达85.4%，具有重要的工业应用和研究价值。