# Online PCB Defect Detector On A New PCB Defect Dataset

arXiv: https://arxiv.org/abs/1902.06197 · HTML: https://mlsanity.com/paper/1902.06197/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 25 of 25)

- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks](https://mlsanity.com/paper/2406.08394/) (2024)
- [MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation](https://mlsanity.com/paper/2602.07011/) (2026)
- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards](https://mlsanity.com/paper/2401.06528/) (2024)
- [Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection](https://mlsanity.com/paper/2109.14020/) (2021)
- [FPIC: A Novel Semantic Dataset for Optical PCB Assurance](https://mlsanity.com/paper/2202.08414/) (2022)
- [ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11](https://mlsanity.com/paper/2502.14314/) (2025)
- [MRC-DETR: An Adaptive Multi-Residual Coupled Transformer for Bare Board PCB Defect Detection](https://mlsanity.com/paper/2507.03386/) (2025)
- [ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories](https://mlsanity.com/paper/2511.20169/) (2025)
- [Enhancing Printed Circuit Board Defect Detection through Ensemble Learning](https://mlsanity.com/paper/2409.09555/) (2024)
- [UniPCB: A Unified Vision-Language Benchmark for Open-Ended PCB Quality Inspection](https://mlsanity.com/paper/2601.19222/) (2026)
- [ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment](https://mlsanity.com/paper/2107.05279/) (2021)
- [Model-based Transfer Learning for Automatic Optical Inspection based on domain discrepancy](https://mlsanity.com/paper/2301.05897/) (2023)
- [YOLO-pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images](https://mlsanity.com/paper/2407.15427/) (2024)
- [ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection](https://mlsanity.com/paper/2506.13476/) (2025)
- [BoardVision: Deployment-ready and Robust Motherboard Defect Detection with YOLO+Faster-RCNN Ensemble](https://mlsanity.com/paper/2510.14389/) (2025)
- [Towards Pixel-Wise Anomaly Location for High-Resolution PCBA via Self-Supervised Image Reconstruction](https://mlsanity.com/paper/2512.17296/) (2025)
- [UniPCB: A Generation-Assisted Vision-Based Measurement Framework for PCB Defect Inspection](https://mlsanity.com/paper/2605.04635/) (2026)
- [From Full Boards to Tiny Defects: Scale-Aware Tile Inference with Topology-Aware Merging for High-Resolution PCB Defect Detection](https://mlsanity.com/paper/2605.24726/) (2026)
- [RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection](https://mlsanity.com/paper/2606.00852/) (2026)
- [DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models](https://mlsanity.com/paper/2606.16601/) (2026)
- [MBA: Multimodal Benchmark and Agents for Real-World Business Ideation](https://mlsanity.com/paper/2608.11616/) (2026)
- [Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering](https://mlsanity.com/paper/2609.21276/) (2026)
- [PCB-MC: Missing Component Analysis in Printed Circuit Boards](https://mlsanity.com/paper/2609.39427/) (2026)

## Abstract (arXiv)

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.