# Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection

arXiv: https://arxiv.org/abs/2409.15980 · HTML: https://mlsanity.com/paper/2409.15980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization](https://mlsanity.com/paper/2011.08785/) (2020)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows](https://mlsanity.com/paper/2107.12571/) (2021)
- [FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows](https://mlsanity.com/paper/2111.07677/) (2021)
- [Deep Industrial Image Anomaly Detection: A Survey](https://mlsanity.com/paper/2301.11514/) (2023)
- [Anomalib: A Deep Learning Library for Anomaly Detection](https://mlsanity.com/paper/2202.08341/) (2022)
- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [A Survey of Visual Sensory Anomaly Detection](https://mlsanity.com/paper/2202.07006/) (2022)
- [Benchmarking Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2205.14852/) (2022)
- [Optimizing PatchCore for Few/many-shot Anomaly Detection](https://mlsanity.com/paper/2307.10792/) (2023)
- [Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection](https://mlsanity.com/paper/2202.12653/) (2022)
- [Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19](https://mlsanity.com/paper/2011.11305/) (2020)

## Built on by (most cited first, 1 of 1)

- [On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing](https://mlsanity.com/paper/2512.13497/) (2025)

## Abstract (arXiv)

Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accuracy. These systems typically require expensive camera, computing equipment and significant machine learning expertise, which can substantially burden small and medium-sized enterprises. This study explores leveraging unsupervised learning methods with pre-trained models and low-cost hardware to create a cost-effective visual anomaly detection system. The research aims to develop a low-cost visual anomaly detection solution that uses minimal data for model training while maintaining generalizability and scalability. The system utilises unsupervised learning models from Anomalib and is deployed on affordable Raspberry Pi hardware through openVINO. The results show that this cost-effective system can complete anomaly defection training and inference on a Raspberry Pi in just 90 seconds using only 10 normal product images, achieving an F1 macro score exceeding 0.95. While the system is slightly sensitive to environmental changes like lighting, product positioning, or background, it remains a swift and economical method for factory automation inspection for small and medium-sized manufacturers. The code is available at https://github.com/Yunbo-max/Cost-Effective-Visual-Anomaly-Detection-using-Unsupervised-Learning.