モバイルロボットのための外れ値露出による視覚的異常検知性能の改善
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
移動ロボットの視覚的異常検知において、少数の異常データを外れ値露出損失で活用し、Real-NVPモデルの性能を向上させる手法を提案。屋内巡回シナリオの新データセットで有効性を実証。
著者: Dario Mantegazza, Alessandro Giusti, Luca Maria Gambardella, Jérôme Guzzi
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements.
