Deformable-Heatmap-Segmentation for Automobile Visual Perception
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 10 Jul 2024.
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References · 12
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- 2014Generalized Max Poolingcited by 12
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