Synthetic outlier generation for anomaly detection in autonomous driving
Martin Bikandi Gorka Velez Naiara Aginako Itziar Irigoien
Anomaly detection, or outlier detection, is a crucial task in various domains
to identify instances that significantly deviate from established patterns or
the majority of data. In the context of autonomous driving, the identification
of anomalies is particularly important to prevent safety-critical incidents, as
deep learning models often exhibit overconfidence in anomalous or outlier
samples. In this study, we explore different strategies for training an image
semantic segmentation model with an anomaly detection module. By introducing
modifications to the training stage of the state-of-the-art DenseHybrid model,
we achieve significant performance improvements in anomaly detection. Moreover,
we propose a simplified detector that achieves comparable results to our
modified DenseHybrid approach, while also surpassing the performance of the
original DenseHybrid model. These findings demonstrate the efficacy of our
proposed strategies for enhancing anomaly detection in the context of
autonomous driving.