RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
HRIPCB: a challenging dataset for PCB defects detection and classification,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
MoEIoU is a mixture-of-experts IoU loss using log-sum-exp aggregation and curriculum weighting that reports consistent gains over prior IoU losses on PASCAL VOC, HRIPCB, and MS COCO with YOLO models.
Tile-based inference with topology-aware merging improves small PCB defect detection by preserving scale and resolving edge artifacts on two datasets.
citing papers explorer
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RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection
RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
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MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts
MoEIoU is a mixture-of-experts IoU loss using log-sum-exp aggregation and curriculum weighting that reports consistent gains over prior IoU losses on PASCAL VOC, HRIPCB, and MS COCO with YOLO models.
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From Full Boards to Tiny Defects: Scale-Aware Tile Inference with Topology-Aware Merging for High-Resolution PCB Defect Detection
Tile-based inference with topology-aware merging improves small PCB defect detection by preserving scale and resolving edge artifacts on two datasets.