The investigation on defect recognition system using gaussian smoothing and template matching approach

This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features is proposed in determining better image processing. Template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. This system does lessen execution time, yet additionally produce high precision in deformity location rate. It is discovered that the proposed framework can give precision at 95.77% recognition rate in recognizing imperfection for gluing application.