  For tiny obstacle discovery in a monocular image, edge is a fundamental
visual element. Nevertheless, because of various reasons, e.g., noise and
similar color distribution with background, it is still difficult to detect the
edges of tiny obstacles at long distance. In this paper, we propose an
obstacle-aware discovery method to recover the missing contours of these
obstacles, which helps to obtain obstacle proposals as much as possible. First,
by using visual cues in monocular images, several multi-layer regions are
elaborately inferred to reveal the distances from the camera. Second, several
novel obstacle-aware occlusion edge maps are constructed to well capture the
contours of tiny obstacles, which combines cues from each layer. Third, to
ensure the existence of the tiny obstacle proposals, the maps from all layers
are used for proposals extraction. Finally, based on these proposals containing
tiny obstacles, a novel obstacle-aware regressor is proposed to generate an
obstacle occupied probability map with high confidence. The convincing
experimental results with comparisons on the Lost and Found dataset demonstrate
the effectiveness of our approach, achieving around 9.5% improvement on the
accuracy than FPHT and PHT, it even gets comparable performance to MergeNet.
Moreover, our method outperforms the state-of-the-art algorithms and
significantly improves the discovery ability for tiny obstacles at long
distance.
