Dynamic Loss Balancing and Sequential Enhancement for Road-Safety Assessment and Traffic Scene Classification

Road-safety inspection is an indispensable instrument for reducing road-accident fatalities related to road infrastructure. Recent work formalizes the assessment procedure in terms of carefully selected risk factors that are also known as road-safety attributes. In current practice, these attributes are manually annotated in geo-referenced monocular video for each road segment. We propose to reduce dependency on tedious human labor by automating attribute collection through a two-stage deep learning approach. The first stage recognizes more than forty road-safety attributes by observing a local spatio-temporal context. Our design leverages an efficient convolutional pipeline, which benefits from pre-training on semantic segmentation of street scenes. The second stage enhances predictions through sequential integration across a larger temporal window. Our design leverages per-attribute instances of a lightweight recurrent architecture. Both stages alleviate extreme class imbalance by incorporating a multi-task variant of recall-based dynamic loss weighting. We perform experiments on the novel iRAP-BH dataset, which involves fully labeled geo-referenced video along 2,300 km of public roads in Bosnia and Herzegovina. Moreover, we evaluate our approach against the related work on three road-scene classification datasets from the literature: Honda Scenes, FM3m, and BDD100k. Experimental evaluation confirms the value of our contributions on all three datasets.