利用无监督学习实现高效视觉异常检测
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection
AI总结：
本研究提出一种低成本视觉异常检测系统，通过预训练模型和低成本硬件，利用少量数据实现高准确率的异常检测，适用于中小型企业。
摘要：
传统的基于机器学习的视觉检测系统需要大量数据收集和重复模型训练来提高准确性。这些系统通常需要昂贵的相机、计算设备和显著的机器学习专业知识，这对中小型企业构成重大负担。本研究探索利用预训练模型和低成本硬件的无监督学习方法，开发一种高效的视觉异常检测系统。该系统利用Anomalib的无监督学习模型，并通过openVINO部署在经济型Raspberry Pi硬件上。结果表明，该系统仅用10张正常产品图像即可在Raspberry Pi上完成异常检测的训练和推理，耗时仅90秒，达到F1宏评分超过0.95的性能。尽管系统对环境变化如光照、产品摆放或背景略有敏感，但其仍为中小型企业提供了一种快速且经济的工厂自动化检测方法。代码可在https://github.com/Yunbo-max/Cost-Effective-Visual-Anomaly-Detection-using-Unsupervised-Learning获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
