# ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detection

arXiv: https://arxiv.org/abs/2109.05746 · HTML: https://mlsanity.com/paper/2109.05746/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 13 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [A PCB Dataset for Defects Detection and Classification](https://mlsanity.com/paper/1901.08204/) (2019)
- [Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives](https://mlsanity.com/paper/2002.04210/) (2020)

## Built on by (most cited first, 6 of 6)

- [Long-Tailed Anomaly Detection with Learnable Class Names](https://mlsanity.com/paper/2403.20236/) (2024)
- [FPIC: A Novel Semantic Dataset for Optical PCB Assurance](https://mlsanity.com/paper/2202.08414/) (2022)
- [Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts](https://mlsanity.com/paper/2507.16946/) (2025)
- [Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background](https://mlsanity.com/paper/2409.00589/) (2024)
- [RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection](https://mlsanity.com/paper/2606.00852/) (2026)
- [Quality Inspection of Printed Circuit Board Pin Insertion via Semantic Segmentation and Board-Level Feature Extraction](https://mlsanity.com/paper/2608.22937/) (2026)

## Abstract (arXiv)

The usage of electronic devices increases, and becomes predominant in most aspects of life. Surface Mount Technology (SMT) is the most common industrial method for manufacturing electric devices in which electrical components are mounted directly onto the surface of a Printed Circuit Board (PCB). Although the expansion of electronic devices affects our lives in a productive way, failures or defects in the manufacturing procedure of those devices might also be counterproductive and even harmful in some cases. It is therefore desired and sometimes crucial to ensure zero-defect quality in electronic devices and their production. While traditional Image Processing (IP) techniques are not sufficient to produce a complete solution, other promising methods like Deep Learning (DL) might also be challenging for PCB inspection, mainly because such methods require big adequate datasets which are missing, not available or not updated in the rapidly growing field of PCBs. Thus, PCB inspection is conventionally performed manually by human experts. Unsupervised Learning (UL) methods may potentially be suitable for PCB inspection, having learning capabilities on the one hand, while not relying on large datasets on the other. In this paper, we introduce ChangeChip, an automated and integrated change detection system for defect detection in PCBs, from soldering defects to missing or misaligned electronic elements, based on Computer Vision (CV) and UL. We achieve good quality defect detection by applying an unsupervised change detection between images of a golden PCB (reference) and the inspected PCB under various setting. In this work, we also present CD-PCB, a synthesized labeled dataset of 20 pairs of PCB images for evaluation of defect detection algorithms.