Reinforced Voxel-RCNN: An Efficient 3D Object Detection Method Based on Feature Aggregation-Reference-Cited by-同舟云学术

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Reinforced Voxel-RCNN: An Efficient 3D Object Detection Method Based on Feature Aggregation

Published:2024-09-01 Issue:9 Volume:E107.D Page:1228-1238
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

JIANG Jia-ji¹,WAN Hai-bin¹,SUN Hong-min²,QIN Tuan-fa¹,WANG Zheng-qiang³

Affiliation:

1. School of Computer, Electronics and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University

2. Guangxi Vocational & Technical Institute of Industry

3. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunication

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Link

https://www.jstage.jst.go.jp/article/transinf/E107.D/9/E107.D_2023EDP7200/_pdf

Reference34 articles.

1. [1] Y. Zhao, Y. Rao, S. Dong, and J. Zhang, “A review of deep learning target detection methods,” Image Graph, vol.25, no.4, pp.629-654, 2020.

2. [2] Y. Amit, P. Felzenszwalb, and R. Girshick, “Object detection,” Computer Vision: A Reference Guide, pp.1-9, 2020. 10.1007/978-3-030-03243-2_660-1

3. [3] C. Tao, J. Cao, C. Wang, Z. Zhang, and Z. Gao, “Pseudo-mono for monocular 3d object detection in autonomous driving,” IEEE Trans. Circuits Syst. Video Technol., vol.33, no.8, pp.3962-3975, 2023. 10.1109/tcsvt.2023.3237579

4. [4] S.Y. Alaba and J.E. Ball, “Deep learning-based image 3d object detection for autonomous driving,” IEEE Sensors J., vol.23, no.4, pp.3378-3394, 2023. 10.1109/JSEN.2023.3235830

5. [5] X. Zhao, P. Sun, Z. Xu, H. Min, and H. Yu, “Fusion of 3d lidar and camera data for object detection in autonomous vehicle applications,” IEEE Sensors J., vol.20, no.9, pp.4901-4913, 2020. 10.1109/jsen.2020.2966034

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