Yunxiang Liu, Haili Ma, Jianlin Zhu*, Qiangbo Zhang
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710
- 15 August 2024
Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >
Login
Register
