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.The Journal of Engineering, 2020(13): 303–309, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.