RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection
Researchers have developed RefDiffNet, a novel input enhancement block designed to improve the detection of subtle defects on printed circuit boards (PCBs). This lightweight module works by comparing a defective PCB image with a defect-free reference image, highlighting structural changes that indicate flaws. When integrated with various deep learning detectors like YOLOv8 and Faster R-CNN, RefDiffNet consistently boosts performance with minimal computational overhead. AI
IMPACT This method could improve manufacturing quality control by enabling earlier and more accurate detection of subtle defects in PCBs.