arXiv Open Access 2024

Deformable-Heatmap-Segmentation for Automobile Visual Perception

Hongyu Jin
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

Penulis (1)

H

Hongyu Jin

Format Sitasi

Jin, H. (2024). Deformable-Heatmap-Segmentation for Automobile Visual Perception. https://arxiv.org/abs/2407.07493

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓