Summary of Leveraging Unsupervised Learning For Cost-effective Visual Anomaly Detection, by Yunbo Long et al.
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection
by Yunbo Long, Zhengyang Ling, Sam Brook, Duncan McFarlane, Alexandra Brintrup
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study explores the development of a cost-effective visual anomaly detection system using unsupervised learning methods with pre-trained models and low-cost hardware. The goal is to create a solution that requires minimal data for model training while maintaining generalizability and scalability. The system utilizes Anomalib’s unsupervised learning models and is deployed on affordable Raspberry Pi hardware through openVINO. Results show the system can complete anomaly detection training and inference in 90 seconds using only 10 normal product images, achieving an F1 macro score exceeding 0.95. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to create a low-cost visual anomaly detection solution that uses minimal data for model training while maintaining generalizability and scalability. The goal is to help small and medium-sized enterprises in the manufacturing industry by reducing costs and increasing efficiency. The system uses pre-trained models and unsupervised learning methods, which makes it quick and affordable. |
Keywords
» Artificial intelligence » Anomaly detection » Inference » Unsupervised
