@inproceedings{fce36eb9f74a4eb5bfca4bacfd0f5387,
title = "Object Detection Based on Hierarchical Multi-view Proposal Network for Autonomous Driving",
abstract = "To achieve better results on object detection for autonomous vehicle under complex outdoor conditions, we attempt to integrated the sensor-fusion, hierarchical multi-view networks and traditional heuristical method together. The most significant environmental perception sensors for autonomous vehicles are camera and LIDAR. The 2D RGB image and 3D point cloud from camera and LIDAR respectively are utilized. The hierarchical multi-view proposal network (HMVPN) is proposed in this paper, which can effectively fuse the multi-modal information of the camera with LIDAR. As there are several hierarchical network layers in HMVPN, image becomes the input of the primary network for object detection. Moreover, LIDAR data is divided into four projection image (HBV, IBV, HCV, DCV), and then combines its original 3D point cloud into hierarchical second network to generate candidate proposals using machine learning and heuristic methods. Several simulations on the famous autonomous vehicle benchmark of KITTI show that our approach obtains about 20\% higher AP than the state-of-the-art methods.",
keywords = "Autonomous driving, Deep learning, Multiview network, Sensor fusion",
author = "Jianhui Zhao and Zhang, \{Xinyu Newman\} and Hongbo Gao and Jialun Yin and Mo Zhou and Chuanqi Tan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489196",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
address = "美国",
}