Clutch Disc Detection Dataset

The current industrial sector faces significant challenges in the automated inspection of clutch discs. Traditional methods are often labor-intensive and prone to human error, leading to inconsistencies in quality control. Existing solutions lack the capability to efficiently assess wear levels and engagement forces, which are critical for performance. This dataset aims to address these technical challenges by providing high-quality images for training machine learning models to automate the detection of clutch disc wear and validate grab forces. Data was collected using high-resolution cameras in a controlled factory environment, ensuring optimal lighting and focus. Quality control measures included multiple rounds of annotation and expert review to enhance precision. The dataset is stored in JPG format, organized by image ID, with metadata to describe each image's attributes. This structured approach facilitates easy access and integration into machine learning pipelines.