Detection of Printed Circuit Board (PCB) Defects Using Deep Learning Approach

Printed circuit boards, or PCBs, are a part of nearly all electronic equipment. The rapid development of integrated circuit and semiconductor technology has made it possible for printed circuit boards (PCBs) to have dimensions as small as a single unit. For this reason, quick and accurate PCB defect detection must be accomplished. Deep learning offers a promising solution for high-precision and rapid defect detection due to its ability to automatically learn complex patterns from data. To identify PCB defects, this research combines deep learning algorithms Faster Region-based Convolutional Neural Network (R-CNN) with computer vision techniques. The study investigates how well different backbone models—ResNet50, MobileNetV2, and EfficientNetB0—work in the Faster R-CNN architecture to accurately detect PCB defects. MobileNetV2 and EfficientNetB0 put computing efficiency first, while ResNet50 is renowned for its depth and capacity to capture fine-grained characteristics. How these changes affect the detection performance will be determined through the comparison analysis. Different backbone architectures provide the model with different computing efficiency and representational capacities, which makes feature extraction and defect detection possible. With the ultimate goal of improving quality control procedures in electronics production, the performance is assessed using an extensive dataset of diverse PCB images. The findings show that it is effective in recognizing and accurately forecasting defects.