Tire Bolt/Nut Detection Dataset

In the automotive industry, ensuring the correct assembly of fasteners is critical for safety and performance. However, many manufacturers face challenges in accurately detecting and verifying the installation of tire bolts and nuts during production and maintenance processes. Existing solutions often rely on manual inspections, which can lead to human errors and inconsistencies. The Tire Bolt/Nut Detection Dataset aims to address these challenges by providing a robust set of labeled images for training machine learning models that automate the detection of these components. The dataset consists of images captured in controlled environments with various lighting conditions and angles to ensure diversity. To maintain high data quality, we employed multi-round annotations, consistency checks, and expert reviews at each stage. The images are stored in JPG format and organized in folders by categories.