用YOLOv9与生成模型结合，实现金属板缺陷高效自动检测
YOLO-Based Defect Detection for Metal Sheets
- 采用YOLOv9搭配ConSinGAN生成数据增强，提升小样本下检测性能
- 检测准确率达91.3%，单次检测仅需146毫秒，满足工业实时需求
- 已部署至制造产线，适用于多种工业部件的缺陷检测
本文提出一种基于YOLO的深度学习模型，用于解决工业制造中耗时且人力密集的缺陷检测问题。实验使用金属板图像作为训练数据，检测表面及孔洞缺陷。由于真实金属板图像数据稀缺，导致检测精度下降。为此，引入ConSinGAN生成大量合成数据，与四种YOLO版本（YOLOv3、v4、v7、v9）结合进行数据增强。结果表明，采用ConSinGAN的YOLOv9模型表现最优，准确率达到91.3%，单次检测耗时146毫秒。该模型已集成至制造硬件与监控与数据采集（SCADA）系统，构建了实用的自动化光学检测（AOI）系统。此外，该方法可便捷应用于其他工业部件的缺陷检测。
原文摘要 · Abstract (English)
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.
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