Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7

Dongzhu Jiang; Keyan Liu; Limin Jia; Yong Qin; Yaopeng Jiang; Zhipeng Wang

doi:10.1016/j.ifacol.2023.10.672

Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7

Dongzhu Jiang^*
, Keyan Liu^*
, Limin Jia^*
, Yong Qin^*
, Yaopeng Jiang^*
, Zhipeng Wang^*

^*Corresponding author for this work

Beijing Jiaotong University

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

5 Scopus citations

Abstract

In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	7597-7602
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.672
State	Published - 1 Jul 2023
Externally published	Yes
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	9/07/23 → 14/07/23

Keywords

Catenary support device
Improved YOLOv7
Multiscale capture strategy
UAV automatic inspection

Access to Document

10.1016/j.ifacol.2023.10.672

Cite this

@inproceedings{a144563ce27b46b5bfaa6e27815fa8d1,

title = "Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7",

abstract = "In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3\% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.",

keywords = "Catenary support device, Improved YOLOv7, Multiscale capture strategy, UAV automatic inspection",

author = "Dongzhu Jiang and Keyan Liu and Limin Jia and Yong Qin and Yaopeng Jiang and Zhipeng Wang",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",

year = "2023",

month = jul,

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doi = "10.1016/j.ifacol.2023.10.672",

language = "英语",

series = "IFAC-PapersOnLine",

publisher = "Elsevier B.V.",

number = "2",

pages = "7597--7602",

editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",

booktitle = "IFAC-PapersOnLine",

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}

Jiang, D, Liu, K, Jia, L, Qin, Y, Jiang, Y & Wang, Z 2023, Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7. in H Ishii, Y Ebihara, J Imura & M Yamakita (eds), IFAC-PapersOnLine. 2 edn, IFAC-PapersOnLine, no. 2, vol. 56, Elsevier B.V., pp. 7597-7602, 22nd IFAC World Congress, Yokohama, Japan, 9/07/23. https://doi.org/10.1016/j.ifacol.2023.10.672

Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7. / Jiang, Dongzhu; Liu, Keyan; Jia, Limin et al.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 7597-7602 (IFAC-PapersOnLine; Vol. 56, No. 2).

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

TY - GEN

T1 - Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7

AU - Jiang, Dongzhu

AU - Liu, Keyan

AU - Jia, Limin

AU - Qin, Yong

AU - Jiang, Yaopeng

AU - Wang, Zhipeng

PY - 2023/7/1

Y1 - 2023/7/1

N2 - In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.

AB - In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.

KW - Catenary support device

KW - Improved YOLOv7

KW - Multiscale capture strategy

KW - UAV automatic inspection

UR - https://www.scopus.com/pages/publications/85184959005

U2 - 10.1016/j.ifacol.2023.10.672

DO - 10.1016/j.ifacol.2023.10.672

M3 - 会议稿件

AN - SCOPUS:85184959005

T3 - IFAC-PapersOnLine

SP - 7597

EP - 7602

BT - IFAC-PapersOnLine

A2 - Ishii, Hideaki

A2 - Ebihara, Yoshio

A2 - Imura, Jun-ichi

A2 - Yamakita, Masaki

PB - Elsevier B.V.

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

Automatic Detection Strategy of Multi-Scale Catenary Support Device Based on Improved YOLOv7

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Keywords

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