Summary of Online-adaptive Anomaly Detection For Defect Identification in Aircraft Assembly, by Siddhant Shete et al.
Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly
by Siddhant Shete, Dennis Mronga, Ankita Jadhav, Frank Kirchner
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 novel framework proposed in this paper leverages transfer learning to improve online-adaptive anomaly detection accuracy in various applications such as autonomous driving and medical diagnosis. The approach adapts to different environments by selecting visually similar training images, fitting a normality model to EfficientNet features, and then computing the Mahalanobis distance between the normality model and test image features. Different similarity measures (SIFT/FLANN, Cosine) and normality models (MVG, OCSVM) are employed and compared with each other. Experimental results on anomaly detection benchmarks and controlled laboratory settings demonstrate a detection accuracy exceeding 0.975, outperforming the state-of-the-art ET-NET approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find unusual things in data by using big models that have already been trained. It’s like training a dog to find a specific type of leaf, then taking it to new places and telling it what kind of leaves to look for. The model can learn from the new leaves and get better at finding unusual ones. The paper tries different ways to do this and shows that its method is really good at finding anomalies. |
Keywords
» Artificial intelligence » Anomaly detection » Transfer learning
