Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection

TL;DR

This paper presents a low-cost, unsupervised learning-based visual anomaly detection system using pre-trained models and affordable hardware, achieving high accuracy with minimal data and rapid training on Raspberry Pi devices.

Contribution

It introduces a scalable, cost-effective anomaly detection approach leveraging unsupervised models and open-source tools on low-cost hardware, suitable for small and medium enterprises.

Findings

Achieves over 0.95 F1 macro score with only 10 normal images.

Training and inference complete in 90 seconds on Raspberry Pi.

System remains effective despite environmental variations.

Abstract

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.