  Semantic segmentation of road elements in 2D images is a crucial task in the
recognition of some static objects such as lane lines and free space. In this
paper, we propose DHSNet,which extracts the objects features with a end-to-end
architecture along with a heatmap proposal. Deformable convolutions are also
utilized in the proposed network. The DHSNet finely combines low-level feature
maps with high-level ones by using upsampling operators as well as downsampling
operators in a U-shape manner. Besides, DHSNet also aims to capture static
objects of various shapes and scales. We also predict a proposal heatmap to
detect the proposal points for more accurate target aiming in the network.
