Implementing Image Processing for Quality Inspection of Car Air Conditioning Vents

Quality inspection in the manufacturing of car air conditioning vents has traditionally relied on human operators, a process prone to subjectivity, inconsistency, and inefficiency due to factors like fatigue and human error. To overcome these limitations, this study proposes an automated quality inspection system using image processing techniques to detect defects such as missing parts and scratches. Using MATLAB, the system integrates image acquisition, enhancement, segmentation, and defect analysis for consistent and accurate inspection. Images are captured under controlled lighting with optimal camera positioning to minimize distortion, and preprocessing techniques such as contrast adjustment, morphological operations, and adaptive thresholding are applied to refine image quality and highlight defects. Extensive validation of the system demonstrated over 90% accuracy in defect detection, particularly when vent positions and angles were fixed. This study highlights the potential of combining image processing and machine vision to improve quality control processes in the automotive industry, offering a reliable alternative to traditional manual inspections.