Image-Consistent Detection of Road Anomalies as Unpredictable Patches

We propose a novel method for anomaly detection primarily aiming at autonomous driving. The design of the method, called DaCUP (Detection of anomalies as Consistent Unpredictable Patches), is based on two general properties of anomalous objects: an anomaly is (i) not from a class that could be modelled and (ii) it is not similar (in appearance) to non-anomalous objects in the image. To this end, we propose a novel embedding bottleneck in an auto-encoder like architecture that enables modelling of a diverse, multi-modal known class appearance (e.g. road). Secondly, we introduce novel image-conditioned distance features that allow known class identification in a nearest-neighbour manner on-the-fly, greatly increasing its ability to distinguish true and false positives. Lastly, an inpainting module is utilized to model the uniqueness of detected anomalies and significantly reduce false positives by filtering regions that are similar, thus reconstructable from their neighbourhood. We demonstrate that filtering of regions based on their similarity to neighbour regions, using e.g. an inpainting module, is general and can be used with other methods for reduction of false positives. The proposed method is evaluated on several publicly available datasets for road anomaly detection and on a maritime benchmark for obstacle avoidance. The method achieves state-of-the-art performance in both tasks with the same hyper-parameters with no domain specific design.