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FCNet: Stereo 3D Object Detection with Feature Correlation Networks

文献类型：期刊论文

作者Wu, Yingyu2; Liu, Ziyan1,2,3; Chen, Yunlei2; Zheng, Xuhui2; Zhang, Qian2; Yang, Mo2; Tang, Guangming1
刊名ENTROPY
出版日期2022-08-01
卷号24期号:8页码:17
关键词3D object detection deep learning stereo matching multi-scale cost-volume channel similarity parallel convolutional attention
DOI10.3390/e24081121
英文摘要Deep-learning techniques have significantly improved object detection performance, especially with binocular images in 3D scenarios. To supervise the depth information in stereo 3D object detection, reconstructing the 3D dense depth of LiDAR point clouds causes higher computational costs and lower inference speed. After exploring the intrinsic relationship between the implicit depth information and semantic texture features of the binocular images, we propose an efficient and accurate 3D object detection algorithm, FCNet, in stereo images. First, we construct a multi-scale cost-volume containing implicit depth information using the normalized dot-product by generating multi-scale feature maps from the input stereo images. Secondly, the variant attention model enhances its global and local description, and the sparse region monitors the depth loss deep regression. Thirdly, for balancing the channel information preservation of the re-fused left-right feature maps and computational burden, a reweighting strategy is employed to enhance the feature correlation in merging the last-layer features of binocular images. Extensive experiment results on the challenging KITTI benchmark demonstrate that the proposed algorithm achieves better performance, including a lower computational cost and higher inference speed in 3D object detection.
资助项目Guizhou Science and Technology Foundation[(2016) 1054] ; Guizhou Province Joint funding Project[LH (2017) 7226] ; Guizhou University Academic New Seeding training and Innovation and Exploration Project[(2017) 5788]
WOS研究方向Physics
语种英语
WOS记录号WOS:000846029000001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/19448]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Liu, Ziyan
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Guizhou Univ, Coll Big Data & Informat Engn, Guiyang 550025, Peoples R China
3.Guizhou Univ, State Key Lab Publ Big Data, Guiyang 550025, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Yingyu,Liu, Ziyan,Chen, Yunlei,et al. FCNet: Stereo 3D Object Detection with Feature Correlation Networks[J]. ENTROPY,2022,24(8):17.
APA Wu, Yingyu.,Liu, Ziyan.,Chen, Yunlei.,Zheng, Xuhui.,Zhang, Qian.,...&Tang, Guangming.(2022).FCNet: Stereo 3D Object Detection with Feature Correlation Networks.ENTROPY,24(8),17.
MLA Wu, Yingyu,et al."FCNet: Stereo 3D Object Detection with Feature Correlation Networks".ENTROPY 24.8(2022):17.

入库方式： OAI收割

来源：计算技术研究所

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