Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.25659 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915531607506944 |
|---|---|
| author | Chou, Po-Heng Wang, Chun-Chi Mao, Wei-Lung |
| author_facet | Chou, Po-Heng Wang, Chun-Chi Mao, Wei-Lung |
| contents | In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YOLO-Based Defect Detection for Metal Sheets Chou, Po-Heng Wang, Chun-Chi Mao, Wei-Lung Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Signal Processing 68T45, 68T07 I.2.10; I.4.7; I.5.4 In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing. |
| title | YOLO-Based Defect Detection for Metal Sheets |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Image and Video Processing Signal Processing 68T45, 68T07 I.2.10; I.4.7; I.5.4 |
| url | https://arxiv.org/abs/2509.25659 |