A dataset for surface defect detection on complex structured parts based on photometric stereo
Abstract Automated Optical Inspection (AOI) technology is crucial for industrial defect detection but struggles with shadows and surface reflectivity, resulting in false positives and missed detections, especially on non-planar parts. To address these issues, a novel defect detection technique based...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04454-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850087752160247808 |
|---|---|
| author | Lin Wu Yu Ran Li Yan Yixing Liu You Song Dongran Han |
| author_facet | Lin Wu Yu Ran Li Yan Yixing Liu You Song Dongran Han |
| author_sort | Lin Wu |
| collection | DOAJ |
| description | Abstract Automated Optical Inspection (AOI) technology is crucial for industrial defect detection but struggles with shadows and surface reflectivity, resulting in false positives and missed detections, especially on non-planar parts. To address these issues, a novel defect detection technique based on deep learning and photometric stereo vision was proposed, along with the creation of the Metal Surface Defect Dataset (MSDD). The proposed Stroboscopic Illuminant Image Acquisition (SIIA) method uses a specially arranged illuminant setup and a Taylor Series Channel Mixer (TSCM) to blend multi-angle illumination images into pseudo-color images. This approach enables end-to-end defect detection using universal object detectors. The method involves mapping color space transformations to spatial domain transformations and utilizing hue randomization for data augmentation. Four object detection methods (FCOS, YOLOv5, YOLOv8, and RT-DETR) were validated on the MSDD, achieving an mAP of 86.1%, surpassing traditional methods. The MSDD includes 138,585 single-channel images and 9,239 mixed images, covering eight defect types. This dataset is essential for automated visual inspection of metal surfaces and is freely accessible for research purposes. |
| format | Article |
| id | doaj-art-549a5dd9d2df4f678a7b72623e9cf1ec |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-549a5dd9d2df4f678a7b72623e9cf1ec2025-08-20T02:43:10ZengNature PortfolioScientific Data2052-44632025-02-0112111610.1038/s41597-025-04454-6A dataset for surface defect detection on complex structured parts based on photometric stereoLin Wu0Yu Ran1Li Yan2Yixing Liu3You Song4Dongran Han5School of Life Sciences, Beijing University of Chinese MedicineSchool of Life Sciences, Beijing University of Chinese MedicineSchool of Humanities, Beijing University of Chinese MedicineSchool of Management, Beijing University of Chinese MedicineSchool of Software, Beihang UniversitySchool of Life Sciences, Beijing University of Chinese MedicineAbstract Automated Optical Inspection (AOI) technology is crucial for industrial defect detection but struggles with shadows and surface reflectivity, resulting in false positives and missed detections, especially on non-planar parts. To address these issues, a novel defect detection technique based on deep learning and photometric stereo vision was proposed, along with the creation of the Metal Surface Defect Dataset (MSDD). The proposed Stroboscopic Illuminant Image Acquisition (SIIA) method uses a specially arranged illuminant setup and a Taylor Series Channel Mixer (TSCM) to blend multi-angle illumination images into pseudo-color images. This approach enables end-to-end defect detection using universal object detectors. The method involves mapping color space transformations to spatial domain transformations and utilizing hue randomization for data augmentation. Four object detection methods (FCOS, YOLOv5, YOLOv8, and RT-DETR) were validated on the MSDD, achieving an mAP of 86.1%, surpassing traditional methods. The MSDD includes 138,585 single-channel images and 9,239 mixed images, covering eight defect types. This dataset is essential for automated visual inspection of metal surfaces and is freely accessible for research purposes.https://doi.org/10.1038/s41597-025-04454-6 |
| spellingShingle | Lin Wu Yu Ran Li Yan Yixing Liu You Song Dongran Han A dataset for surface defect detection on complex structured parts based on photometric stereo Scientific Data |
| title | A dataset for surface defect detection on complex structured parts based on photometric stereo |
| title_full | A dataset for surface defect detection on complex structured parts based on photometric stereo |
| title_fullStr | A dataset for surface defect detection on complex structured parts based on photometric stereo |
| title_full_unstemmed | A dataset for surface defect detection on complex structured parts based on photometric stereo |
| title_short | A dataset for surface defect detection on complex structured parts based on photometric stereo |
| title_sort | dataset for surface defect detection on complex structured parts based on photometric stereo |
| url | https://doi.org/10.1038/s41597-025-04454-6 |
| work_keys_str_mv | AT linwu adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yuran adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT liyan adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yixingliu adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yousong adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT dongranhan adatasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT linwu datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yuran datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT liyan datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yixingliu datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT yousong datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo AT dongranhan datasetforsurfacedefectdetectiononcomplexstructuredpartsbasedonphotometricstereo |