# A PCB Dataset for Defects Detection and Classification

arXiv: https://arxiv.org/abs/1901.08204 · HTML: https://mlsanity.com/paper/1901.08204/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 16 of 16)

- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection](https://mlsanity.com/paper/2109.05746/) (2021)
- [Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey](https://mlsanity.com/paper/2312.06718/) (2023)
- [NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds](https://mlsanity.com/paper/2206.11736/) (2022)
- [PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards](https://mlsanity.com/paper/2401.06528/) (2024)
- [MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context](https://mlsanity.com/paper/2412.16897/) (2024)
- [On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs](https://mlsanity.com/paper/2101.11164/) (2021)
- [FPIC: A Novel Semantic Dataset for Optical PCB Assurance](https://mlsanity.com/paper/2202.08414/) (2022)
- [How to Train an Accurate and Efficient Object Detection Model on Any Dataset](https://mlsanity.com/paper/2211.17170/) (2022)
- [Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment](https://mlsanity.com/paper/2403.10569/) (2024)
- [Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset](https://mlsanity.com/paper/2512.24160/) (2025)
- [Open Stamped Parts Dataset](https://mlsanity.com/paper/2403.10369/) (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)
- [Towards Pixel-Wise Anomaly Location for High-Resolution PCBA via Self-Supervised Image Reconstruction](https://mlsanity.com/paper/2512.17296/) (2025)
- [Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection](https://mlsanity.com/paper/2607.12245/) (2026)
- [PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images](https://mlsanity.com/paper/2608.27923/) (2026)

## Abstract (arXiv)

To coupe with the difficulties in the process of inspection and classification of defects in Printed Circuit Board (PCB), other researchers have proposed many methods. However, few of them published their dataset before, which hindered the introduction and comparison of new methods. In this paper, we published a synthesized PCB dataset containing 1386 images with 6 kinds of defects for the use of detection, classification and registration tasks. Besides, we proposed a reference based method to inspect and trained an end-to-end convolutional neural network to classify the defects. Unlike conventional approaches that require pixel-by-pixel processing, our method firstly locate the defects and then classify them by neural networks, which shows superior performance on our dataset.