PCB defect detection based on PSO-optimized threshold segmentation and SURF features

This paper proposes an automated inspection approach for printed circuit boards (PCBs) that can accurately locate defects to solve the issues of low precision, complex equipment, and high cost. Digital image processing techniques are utilized in this method, including filtering, image segmentation, feature extraction, alignment, and mathematical morphology processing. To overcome the Otsu thresholding segmentation algorithm's high computational cost and poor real-time performance, a particle swarm approach is optimized to increase image segmentation efficiency. Meanwhile, combining the benefits of the FLANN algorithm and the SURF method, matching image feature points is done based on the SURF algorithm. The performance of matching image feature points is improved. In addition, the alignment error of the images is reduced. According to experimental results, the improved PCB defect detection algorithm demonstrated 98.9% accuracy, with remarkable efficiency and accuracy, and can satisfy PCB defect detection requirements.