Oven Door Hinge Looseness Detection Dataset

As industrial automation progresses, the safety and stability of equipment have become particularly important. Loosening of oven door hinges not only affects product quality but also poses safety hazards. However, current solutions for hinge looseness detection lack sufficient effectiveness and accuracy, leading to the inability to timely identify potential risks. Therefore, this dataset aims to support the application of machine learning models in hinge looseness detection tasks through high-quality image data. The data collection was carried out using high-resolution cameras in a standardized production line environment, ensuring clear and representative images. Multiple rounds of annotation and consistency checks were implemented to ensure the accuracy and reliability of the data. The data is stored in JPEG format and organized by image ID to facilitate further processing and analysis. The core advantage of the dataset is high annotation accuracy and consistency, with data quality enhanced through an expert review process, achieving more than 95% annotation consistency. Utilizing new data augmentation techniques can effectively enhance the model's generalization ability, ultimately improving detection accuracy by 20% over traditional methods and significantly reducing the miss rate. This dataset provides a reliable tool for the industrial field, contributing to more efficient quality control.