Intelligent Vehicle Collision-Avoidance System with Deep Learning

Yeong Kang Lai; Chu Ying Ho; Yu Hau Huang; Chuan Wei Huang; Yi Xian Kuo; Yu Chieh Chung

doi:10.1109/APCCAS.2018.8605622

Intelligent Vehicle Collision-Avoidance System with Deep Learning

Yeong Kang Lai
, Chu Ying Ho
, Yu Hau Huang
, Chuan Wei Huang
, Yi Xian Kuo
, Yu Chieh Chung

National Chung Hsing University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	123-126
Number of pages	4
ISBN (Electronic)	9781538682401
DOIs	https://doi.org/10.1109/APCCAS.2018.8605622
State	Published - 02 07 2018
Externally published	Yes
Event	14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 - Chengdu, China Duration: 26 10 2018 → 30 10 2018

Publication series

Name	2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018

Conference

Conference	14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018
Country/Territory	China
City	Chengdu
Period	26/10/18 → 30/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

NVIDIA jetson TX2
deep-learning
vehicle collision warning system

Access to Document

10.1109/APCCAS.2018.8605622

Cite this

Lai, Y. K., Ho, C. Y., Huang, Y. H., Huang, C. W., Kuo, Y. X., & Chung, Y. C. (2018). Intelligent Vehicle Collision-Avoidance System with Deep Learning. In 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 (pp. 123-126). Article 8605622 (2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCCAS.2018.8605622

@inproceedings{26629685da4a4c36914e4d4313f7c108,

title = "Intelligent Vehicle Collision-Avoidance System with Deep Learning",

abstract = "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.",

keywords = "NVIDIA jetson TX2, deep-learning, vehicle collision warning system",

author = "Lai, \{Yeong Kang\} and Ho, \{Chu Ying\} and Huang, \{Yu Hau\} and Huang, \{Chuan Wei\} and Kuo, \{Yi Xian\} and Chung, \{Yu Chieh\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018 ; Conference date: 26-10-2018 Through 30-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/APCCAS.2018.8605622",

language = "英语",

series = "2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "123--126",

booktitle = "2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018",

address = "美国",

}

Lai, YK, Ho, CY, Huang, YH, Huang, CW, Kuo, YX & Chung, YC 2018, Intelligent Vehicle Collision-Avoidance System with Deep Learning. in 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018., 8605622, 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 123-126, 14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018, Chengdu, China, 26/10/18. https://doi.org/10.1109/APCCAS.2018.8605622

Intelligent Vehicle Collision-Avoidance System with Deep Learning. / Lai, Yeong Kang; Ho, Chu Ying; Huang, Yu Hau et al.
2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 123-126 8605622 (2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Intelligent Vehicle Collision-Avoidance System with Deep Learning

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AU - Ho, Chu Ying

AU - Huang, Yu Hau

AU - Huang, Chuan Wei

AU - Kuo, Yi Xian

AU - Chung, Yu Chieh

PY - 2018/7/2

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N2 - 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.

AB - 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.

KW - NVIDIA jetson TX2

KW - deep-learning

KW - vehicle collision warning system

UR - https://www.scopus.com/pages/publications/85062210107

U2 - 10.1109/APCCAS.2018.8605622

DO - 10.1109/APCCAS.2018.8605622

M3 - 会议稿件

AN - SCOPUS:85062210107

T3 - 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018

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BT - 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018

Y2 - 26 October 2018 through 30 October 2018

ER -

Lai YK, Ho CY, Huang YH, Huang CW, Kuo YX, Chung YC. Intelligent Vehicle Collision-Avoidance System with Deep Learning. In 2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 123-126. 8605622. (2018 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2018). doi: 10.1109/APCCAS.2018.8605622

Intelligent Vehicle Collision-Avoidance System with Deep Learning

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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