Abstract
Anomaly detection deals with detecting deviations from established patterns within data. It has various applications like autonomous driving, predictive maintenance, and medical diagnosis. To improve anomaly detection accuracy, transfer learning can be applied to large, pre-trained models and adapt them to the specific application context. In this paper, we propose a novel framework for online-adaptive anomaly detection using transfer learning. The approach adapts to different environments by selecting visually similar training images and online fitting a normality model to EfficientNet features extracted from the training subset. Anomaly detection is then performed by computing the Mahalanobis distance between the normality model and the test image features. Different similarity measures (SIFT/FLANN, Cosine) and normality models (MVG, OCSVM) are employed and compared with each other. We evaluate the approach on different anomaly detection benchmarks and data collected in controlled laboratory settings. Experimental results showcase a detection accuracy exceeding 0.975, outperforming the state-of-the-art ET-NET approach.
Abstract (translated)
异常检测涉及在数据中检测与既定模式的偏差。它有许多应用，如自动驾驶、预测维护和医学诊断。为了提高异常检测准确性，可以应用迁移学习对大型预训练模型进行改进，并将它们应用于特定应用场景。在本文中，我们提出了一个利用迁移学习进行在线适应异常检测的新框架。该方法通过选择视觉上相似的训练图像，并将规范化模型与从训练集提取的EfficientNet特征对齐，来适应不同的环境。然后通过计算规范化模型和测试图像特征之间的马哈兰诺夫距离来执行异常检测。比较了不同的相似度度量（SIFT/FLANN，Cosine）和规范化模型（MVG，OCSVM）。我们在不同的异常检测基准测试和由控制实验室设置收集的数据上评估了该方法。实验结果表明，检测精度超过0.975，超过了最先进的ET-NET方法。
URL
https://arxiv.org/abs/2406.12698
