  Interactive devices with touch screen have become commonly used in various
aspects of daily life, which raises the demand for high production quality of
touch screen glass. While it is desirable to develop effective defect detection
technologies to optimize the automatic touch screen production lines, the
development of these technologies suffers from the lack of publicly available
datasets. To address this issue, we in this paper propose a dedicated touch
screen glass defect dataset which includes seven types of defects and consists
of 2504 images captured in various scenarios.All data are captured with
professional acquisition equipment on the fixed workstation. Additionally, we
benchmark the CNN- and Transformer-based object detection frameworks on the
proposed dataset to demonstrate the challenges of defect detection on
high-resolution images. Dataset and related code will be available at
https://github.com/Yangr116/SSGDataset.
