Reference-Based Detection and Classification of Printed Circuit Boards Defects Using Deep Learning and Image Processing Techniques

Printed Circuit Boards (PCBs) are essential components in modern electronic assemblies, where even minor defects can lead to significant performance degradation or total system failure. This paper presents a novel, automated framework for defect detection and classification in PCBs, based on a reference comparison approach integrated with deep learning techniques. The methodology involves aligning each test PCB image with a defect-free reference, performing image pre-processing to highlight discrepancies, and applying a YOLO-based convolutional neural network for defect localization and categorization. The model is trained to identify various defect types, such as missing holes, spurs, open circuits, and short circuits, by learning from annotated visual patterns. Preprocessing steps include grayscale transformation, morphological operations, image differencing, and bounding box annotation for supervised training. Experimental results demonstrate the system's high detection accuracy and robustness across multiple defect classes, confirming its potential for real-time application in industrial quality control processes.