Deformable-Heatmap-Segmentation for Automobile Visual Perception
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916318577426432 |
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| author | Jin, Hongyu |
| author_facet | Jin, Hongyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deformable-Heatmap-Segmentation for Automobile Visual Perception Jin, Hongyu Computer Vision and Pattern Recognition 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. |
| title | Deformable-Heatmap-Segmentation for Automobile Visual Perception |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.07493 |