PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
Online pcb defect detector on a new pcb defect dataset
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Previous works for PCB defect detection based on image difference and image processing techniques have already achieved promising performance. However, they sometimes fall short because of the unaccounted defect patterns or over-sensitivity about some hyper-parameters. In this work, we design a deep model that accurately detects PCB defects from an input pair of a detect-free template and a defective tested image. A novel group pyramid pooling module is proposed to efficiently extract features of a large range of resolutions, which are merged by group to predict PCB defect of corresponding scales. To train the deep model, a dataset is established, namely DeepPCB, which contains 1,500 image pairs with annotations including positions of 6 common types of PCB defects. Experiment results validate the effectiveness and efficiency of the proposed model by achieving $98.6\%$ mAP @ 62 FPS on DeepPCB dataset. This dataset is now available at: https://github.com/tangsanli5201/DeepPCB.
citation-role summary
dataset 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
dataset 1polarities
use dataset 1representative citing papers
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.
Tile-based inference with topology-aware merging improves small PCB defect detection by preserving scale and resolving edge artifacts on two datasets.
UniPCB uses multi-modal conditioned diffusion to synthesize PCB defects and an inverted residual shift attention detector to reach 98.0% mAP@0.5 and 61.8% mAP@0.5:0.95 on DsPCBSD+.
citing papers explorer
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PCB-QA: Evaluating LLMs over the First Printed Circuit Board Design Question-Answer Dataset
PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
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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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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.
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UniPCB: A Generation-Assisted Detection Framework for PCB Defect Inspection
UniPCB uses multi-modal conditioned diffusion to synthesize PCB defects and an inverted residual shift attention detector to reach 98.0% mAP@0.5 and 61.8% mAP@0.5:0.95 on DsPCBSD+.