TD‐YOLO: A Lightweight Detection Algorithm for Tiny Defects in High‐Resolution PCBs

Abstract Printing circuit board (PCB) defect inspection precisely and efficiently is an essential and challenging issue. Therefore, based on several improvements upon YOLOv5‐nano, a novel lightweight detector named TD‐YOLO is proposed to inspect tiny defects in PCBs. First, the lightweight ShuffleNet block is implemented into the backbone to effectively reduce the model weight. Second, novel anchors are designed using modified k‐means clustering to accelerate the model convergence and yield superior detection precision. Then, data augmentation strategy is recomposed by rejecting mosaic augmentation to suppress the emergence of extremely tiny targets. Finally, a mighty feature pyramid network namely MPANet, is newly proposed to boost the feature fusion capability of the model. The experiment results denote TD‐YOLO achieves the highest 99.5% mean average precision on our dataset, outperforming other state of the arts. Specially, the detection metrics for the smallest two defects, such as spur and mouse bite, are increased by 2.1% and 1.2%, respectively, compared with YOLOv5‐nano. Besides, TD‐YOLO has only 1.33 million parameters, decreased by 25% than the baseline. Using a mediocre processor, the detection speed is boosted by 20%, reaching 37 frames per second for the input size of 22402240 pixels.