基于特征融合的多层次多模态目标检测

作者：Cheng Teng^*; Sun Lei; Hou Dengchao; Shi Qin; Zhang Junning; Chen Jiong; Huang He

来源：Qiche Gongcheng/automotive Engineering, 2021, 43(11): 1602-1610.

DOI：10.19562/j.chinasae.qcgc.2021.11.005

• Autonomous driving
• Environmental perception
• Hierarchical feature fusion
• Multimodal fusion
• Small target detection

For the problems of low robustness of the environment perception and identification difficulty of small targets of autonomous driving in complex environment, a multi-level and multi-modal fusion method based on feature fusion is proposed in this paper. Firstly, the image and point cloud modal information are mapped to the same dimension, and the hierarchical features of different size targets are extracted. On this basis, the multi-modal multi-level feature fusion is carried out. Then, six comparative experiments are designed to verify the effectiveness of each module. Finally, the Waymo data set and NIO real car data are used for training and testing. The test results show that the detection MAP value of the network is improved by 23.1% compared with that of YOLO V3. ? 2021, Society of Automotive Engineers of China. All right reserved.

• 单位
  合肥工业大学