Road Anomaly Detection by Partial Image Reconstruction With Segmentation Coupling

Tomas Vojir, Tomas Sipka, Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino, Jiri Matas

Abstract:

We present a novel approach to the detection of unknown
objects in the context of autonomous driving. The problem
is formulated as anomaly detection, since we assume that
the unknown stuff or object appearance cannot be learned.
To that end, we propose a reconstruction module that can be
used with many existing semantic segmentation networks,
and that is trained to recognize and reconstruct road (drivable) surface from a small bottleneck. We postulate that
poor reconstruction of the road surface is due to areas that
are outside of the training distribution, which is a strong indicator of an anomaly. The road structural similarity error
is coupled with the semantic segmentation to incorporate
information from known classes and produce final per-pixel
anomaly scores. The proposed JSR-Net was evaluated on
four datasets, Lost-and-found, Road Anomaly, Road Obstacles, and FishyScapes, achieving state-of-art performance
on all, reducing the false positives significantly, while typically having the highest average precision for wide range
of operation points.