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SECOND: Sparsely Embedded Convolutional Detection

1,2, 1,* and 2
1
State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
2
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; https://doi.org/10.3390/s18103337
Received: 20 August 2018 / Revised: 29 September 2018 / Accepted: 1 October 2018 / Published: 6 October 2018
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Abstract

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

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