Vehicle Detection under UAV Based on Optimal Dense YOLO Method

Zhi Xu; Haochen Shi; Ning Li; Chao Xiang; Huiyu Zhou

doi:10.1109/ICSAI.2018.8599403

Vehicle Detection under UAV Based on Optimal Dense YOLO Method

Zhi Xu, Haochen Shi, Ning Li^*, Chao Xiang, Huiyu Zhou

^*Corresponding author for this work

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

19 Citations (Scopus)

Abstract

In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.

Original language	English
Title of host publication	2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	407-411
Number of pages	5
ISBN (Electronic)	9781728101200
ISBN (Print)	978-1-7281-0121-7
DOIs	https://doi.org/10.1109/ICSAI.2018.8599403
Publication status	Published - 03 Jan 2019
Externally published	Yes
Event	5th International Conference on Systems and Informatics, ICSAI 2018 - Nanjing, China Duration: 10 Nov 2018 → 12 Nov 2018

Publication series

Name	2018 5th International Conference on Systems and Informatics, ICSAI 2018

Conference

Conference	5th International Conference on Systems and Informatics, ICSAI 2018
Country/Territory	China
City	Nanjing
Period	10/11/2018 → 12/11/2018

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education.

Publisher Copyright:
© 2018 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

deep learning
UAV
vehicle detection
YOLO

ASJC Scopus subject areas

Hardware and Architecture
Computer Networks and Communications
Energy Engineering and Power Technology
Control and Systems Engineering

Access to Document

10.1109/ICSAI.2018.8599403Licence: Unspecified

Cite this

Xu, Z., Shi, H., Li, N., Xiang, C., & Zhou, H. (2019). Vehicle Detection under UAV Based on Optimal Dense YOLO Method. In 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings (pp. 407-411). [8599403] (2018 5th International Conference on Systems and Informatics, ICSAI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSAI.2018.8599403

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title = "Vehicle Detection under UAV Based on Optimal Dense YOLO Method",

abstract = "In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.",

keywords = "deep learning, UAV, vehicle detection, YOLO",

author = "Zhi Xu and Haochen Shi and Ning Li and Chao Xiang and Huiyu Zhou",

note = "Funding Information: ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education. Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 5th International Conference on Systems and Informatics, ICSAI 2018 ; Conference date: 10-11-2018 Through 12-11-2018",

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doi = "10.1109/ICSAI.2018.8599403",

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series = "2018 5th International Conference on Systems and Informatics, ICSAI 2018",

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Xu, Z, Shi, H, Li, N, Xiang, C & Zhou, H 2019, Vehicle Detection under UAV Based on Optimal Dense YOLO Method. in 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings., 8599403, 2018 5th International Conference on Systems and Informatics, ICSAI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 407-411, 5th International Conference on Systems and Informatics, ICSAI 2018, Nanjing, China, 10/11/2018. https://doi.org/10.1109/ICSAI.2018.8599403

Vehicle Detection under UAV Based on Optimal Dense YOLO Method. / Xu, Zhi; Shi, Haochen; Li, Ning et al.

2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 407-411 8599403 (2018 5th International Conference on Systems and Informatics, ICSAI 2018).

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

TY - GEN

T1 - Vehicle Detection under UAV Based on Optimal Dense YOLO Method

AU - Xu, Zhi

AU - Shi, Haochen

AU - Li, Ning

AU - Xiang, Chao

AU - Zhou, Huiyu

N1 - Funding Information: ACKNOWLEDGMENT This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (No 20175152036). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education. Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2019/1/3

Y1 - 2019/1/3

N2 - In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.

AB - In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles' detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.

KW - deep learning

KW - UAV

KW - vehicle detection

KW - YOLO

U2 - 10.1109/ICSAI.2018.8599403

DO - 10.1109/ICSAI.2018.8599403

M3 - Conference contribution

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SN - 978-1-7281-0121-7

T3 - 2018 5th International Conference on Systems and Informatics, ICSAI 2018

SP - 407

EP - 411

BT - 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Systems and Informatics, ICSAI 2018

Y2 - 10 November 2018 through 12 November 2018

ER -

Xu Z, Shi H, Li N, Xiang C, Zhou H. Vehicle Detection under UAV Based on Optimal Dense YOLO Method. In 2018 5th International Conference on Systems and Informatics (ICSAI 2018): Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 407-411. 8599403. (2018 5th International Conference on Systems and Informatics, ICSAI 2018). doi: 10.1109/ICSAI.2018.8599403

Vehicle Detection under UAV Based on Optimal Dense YOLO Method

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this