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.

翻译：本文提出一种基于YOLO的深度学习模型，用于实现自动化缺陷检测，以解决工业制造中耗时耗力的人工检测任务。实验中，我们采用金属板材图像作为训练数据集，使YOLO模型能够检测金属板材表面及孔洞处的缺陷。然而，金属板材图像的严重不足会显著降低检测精度。为解决此问题，我们采用ConSinGAN生成大量数据，并将YOLO模型的四个版本（即YOLOv3、v4、v7和v9）与ConSinGAN结合进行数据增强。实验表明，结合ConSinGAN的YOLOv9模型在各项YOLO模型中表现最优，准确率达到91.3%，检测时间为146毫秒。所提出的YOLOv9模型已集成至制造硬件与监控与数据采集系统中，构建了实用的自动光学检测系统。此外，该自动化缺陷检测方案可便捷地推广至工业制造中的其他零部件检测场景。