  Road-safety inspection is an indispensable instrument for reducing
road-accident fatalities contributed to road infrastructure. Recent work
formalizes road-safety assessment 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 recognition with a two-stage neural architecture. The first stage
predicts 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 bidirectional LSTM architecture. Both stages alleviate extreme
class imbalance by incorporating a multi-task variant of recall-based dynamic
loss weighting. We perform experiments on the iRAP-BH dataset, which involves
fully labeled geo-referenced video along 2,300 km of public roads in Bosnia and
Herzegovina. We also validate our approach by comparing it with the related
work on two road-scene classification datasets from the literature: Honda
Scenes and FM3m. Experimental evaluation confirms the value of our
contributions on all three datasets.
