SSGD: A smartphone screen glass dataset for defect detection
This work addresses a domain-specific problem for manufacturers needing automated quality control in touch screen production, but it is incremental as it primarily provides a new dataset.
The authors tackled the lack of publicly available datasets for touch screen glass defect detection by creating SSGD, a dataset with 2504 images covering seven defect types, captured under various scenarios, and benchmarked CNN- and Transformer-based object detection frameworks to highlight challenges in high-resolution defect detection.
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.