Intelligent Vehicle Collision-Avoidance System with Deep Learning

被引：0

作者：

Lai, Yeong-Kang ^{[1
]}

Ho, Chu-Ying ^{[1
]}

Huang, Yu-Hau ^{[1
]}

Huang, Chuan-Wei ^{[1
]}

Kuo, Yi-Xian ^{[1
]}

Chung, Yu-Chieh ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung, Taiwan

来源：

2018 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS (APCCAS 2018) | 2018年

关键词：

vehicle collision warning system; deep-learning; nvidia jetson TX2;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we demonstrate and evaluate a method to perform real-time object detection with unmanned vehicle using the state of the art, MobileNets, with object detection algorithm running on an NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications that use neural networks under the hood. This, as a result of comparing several cutting edge object detection algorithms. Multiple evaluations we present provide insights that help choose the optimal object detection configuration given certain frame rate and detection accuracy requirements. We propose how this setup running on-board a unmanned vehicle can be used to process a video feedback during emergencies in real-time, and feed a decision support warning system using the generated detections.

引用

页码：123 / 126

页数：4

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