Printed circuit board defect detection for smart manufacturing using a fast integral image approach

The inspection speed of a system directly affects the production rate and quality of the parts produced. There are various ways of processing inspected data at different inspection speeds, but at present, inspection speeds are relatively slow and not up to the industry standard. This study aims to investigate the effect of inspection speed on production speed and product quality. The research work introduces a quick and efficient method of defect detection on printed circuit boards (PCBs) by using an integral image technique. This method efficiently computes region-based features, making it quick and accurate in defect detection on PCBs. The proposed methodology for defect detection on PCBs was evaluated on the DeepPCB dataset using an integral image technique, and the results obtained were an accuracy of 97.86%, precision of 98.31%, recall of 91.80%, and F1-score of 95.04%. It was concluded that the proposed method, which has low computational complexity, is competitive relative to other state-of-the-art defect detection methods on PCBs. The proposed integral-image-based approach holds significance because it enables much faster and more efficient PCB inspection, thereby supporting higher production rates and improved product quality. By achieving high accuracy with low computational complexity, it offers a practical and competitive alternative to existing state-of-the-art defect detection methods in modern manufacturing.