Car Body Paint Defect Inspection Using Rotation Invariant Measure of the Local Variance and One-Against-All Support Vector Machine

Abstract

This paper presents a novel computer vision method for automatic detection and classification of car body paint defects. This new system analyzes the images sequentially acquired from car body to detect and classify different kinds of defects. First, the defect region is located by using rotation invariant measure of the local variance (VAR) operator. Next, detected defects are classified into different defect types by using One-Against-All Support Vector Machine (OAA-SVM) classifier. The experimental results demonstrated the effectiveness of the proposed approach.