Deformable-Heatmap-Segmentation for Automobile Visual Perception
<|reference_start|>Deformable-Heatmap-Segmentation for Automobile Visual Perception: 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.<|reference_end|>
@article{jin2024deformable-heatmap-segmentation,
    title={Deformable-Heatmap-Segmentation for Automobile Visual Perception},
    author={Hongyu Jin},
    journal={arXiv preprint arXiv:2407.07493},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07493},
    primaryClass={cs.CV}
}