Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly
This work addresses defect identification in aircraft assembly, which is an incremental improvement in domain-specific anomaly detection.
The paper tackled the problem of anomaly detection for defect identification in aircraft assembly by proposing an online-adaptive framework using transfer learning, achieving a detection accuracy exceeding 0.975 and outperforming the state-of-the-art ET-NET approach.
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.