We present the Open Stamped Parts Dataset (OSPD), featuring synthetic and
real images of stamped metal sheets for auto manufacturing. The real part
images, captured from 7 cameras, consist of 7,980 unlabeled images and 1,680
labeled images. In addition, we have compiled a defect dataset by overlaying
synthetically generated masks on 10% of the holes. The synthetic dataset
replicates the real manufacturing environment in terms of lighting and part
placement relative to the cameras. The synthetic data includes 7,980 training
images, 1,680 validation images and 1,680 test images, each with bounding box
and segmentation mask annotations around all holes. 10% of the holes in the
synthetic data mimic defects generated in the real image dataset. We trained a
hole-detection model on the synthetic-OSPD, achieving a modified recall score
of 67.2% and a precision of 94.4% . We anticipate researchers in the auto
manufacturing and broader machine learning and computer vision communities
using OSPD to advance the state of the art in defect detection of stamped holes
in the metalsheet stamping process. The dataset is available for download at:
this https URL

我们提供了一个包含合成和真实图像的开放冲压零部件数据集 (OSPD)，该数据集可用于汽车制造。我们通过 7 台摄像机拍摄到的真实部件图像包括 7980 张未标记的图像和 1680 张标记的图像。此外，我们还通过在 10% 的孔洞上覆盖合成生成的缺陷掩模来编制了一个缺陷数据集。合成数据集在照明和零件相对于摄像机的放置方面复制了真实的制造环境。该合成数据包括 7980 张训练图像、1680 张验证图像和 1680 张测试图像，每张图像上都有围绕所有孔洞的边界框和分割掩模注释。合成数据中的 10% 的孔洞模仿了真实图像数据集中生成的缺陷。我们在合成 - OSPD 上训练了一个孔洞检测模型，实现了 67.2% 的改进召回率和 94.4% 的精度。我们预计汽车制造和更广泛的机器学习和计算机视觉社区的研究人员将使用 OSPD 推动冲压零件中的缺陷检测的现有技术。该数据集可在以下链接下载：this https URL。