Car Window Glass Detection Dataset

In the industrial sector, the need for ensuring the quality and safety of car window glasses has become paramount due to rising consumer expectations and regulatory standards. However, current automated systems often fall short in accurately detecting defects, leading to potential safety hazards. This dataset aims to bridge this gap by providing high-quality images for training machine learning models to reliably detect and classify various defects in car window glasses. The data was collected using high-resolution cameras in controlled environments, ensuring consistency. Quality control measures, including multi-round annotations and expert reviews, were implemented to enhance the dataset's reliability. The images are stored in JPG format, organized by categories of defects and conditions.