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SECOND: Sparsely Embedded Convolutional Detection

by 1,2, 1,* and 2
State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
TrunkTech Co., Ltd., No. 3, Danling street, ZhongGuan Town, HaiDian District, Beijing 100089, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3337;
Received: 20 August 2018 / Revised: 29 September 2018 / Accepted: 1 October 2018 / Published: 6 October 2018
(This article belongs to the Section Remote Sensors)
LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when processing point cloud LiDAR data. However, problems remain, including a slow inference speed and low orientation estimation performance. We therefore investigate an improved sparse convolution method for such networks, which significantly increases the speed of both training and inference. We also introduce a new form of angle loss regression to improve the orientation estimation performance and a new data augmentation approach that can enhance the convergence speed and performance. The proposed network produces state-of-the-art results on the KITTI 3D object detection benchmarks while maintaining a fast inference speed. View Full-Text
Keywords: 3D object detection; convolutional neural networks; LIDAR; autonomous driving 3D object detection; convolutional neural networks; LIDAR; autonomous driving
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MDPI and ACS Style

Yan, Y.; Mao, Y.; Li, B. SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018, 18, 3337.

AMA Style

Yan Y, Mao Y, Li B. SECOND: Sparsely Embedded Convolutional Detection. Sensors. 2018; 18(10):3337.

Chicago/Turabian Style

Yan, Yan, Yuxing Mao, and Bo Li. 2018. "SECOND: Sparsely Embedded Convolutional Detection" Sensors 18, no. 10: 3337.

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