Automatic Paint Defect Detection and Classification of Car Body

Nowadays on line automatic inspection plays an important role in industrial quality management. This paper proposes a new computer vision system for automatic painted car body inspection in the context of quality control in industrial manufacturing. In most worldwide automotive industries, the inspection process is still mainly performed by human vision, and thus, is insufficient and costly. Therefore, automatic paint defect inspection is required to reduce the cost and time waste caused by defects. This new system analyzes the images sequentially acquired from car body to detect different kinds of defects. Initially, defects are detected and localized by using a joint distribution of local binary pattern (LBP) and rotation invariant measure of the local variance (VAR) operators and next, detected defects are classified into different defect types by using Bayesian classifier. The results show that this method could detect defects and classify them with high accuracy. Because of its simplicity, online implementation is possible as well.