Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection
This provides a cost-effective solution for small and medium-sized enterprises in factory automation, though it is incremental as it adapts existing methods to new hardware and data constraints.
The study tackled the problem of high costs in visual inspection systems by developing a low-cost anomaly detection system using unsupervised learning and affordable hardware, achieving an F1 macro score over 0.95 with training and inference in 90 seconds using only 10 normal images.
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