Multi-Objective Performance Evaluation of the Detection of Catenary Support Components Using DCNNs

Wenqiang Liu, Zhigang Liu, Alfredo Nunez, Liyou Wang, Kai Liu, Yang Lyu, Hongrui Wang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)
101 Downloads (Pure)

Abstract

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about defects in the catenary support devices. A series of experiments in an unified test environment for detection of components are performed using Faster-CNN, R-FCN, SSD, and YOLOv2. Through the comparison of different assessment indicators, such as precision, recall, average precision and mean average precision, the detection performance of the different DCNNs methods for the components of the catenary support devices is analyzed, discussed and evaluated. The experiment results show that among all considered methods, R-FCN is the more suitable for the detection of catenary support components.
Original languageEnglish
Title of host publicationProceedings of 15th IFAC Symposium on Control in Transportation Systems (CTS 2018)
Subtitle of host publicationSavona, Italy, June 6-8, 2018
EditorsBart De Schutter , Antonella Ferrara
Pages98-105
Number of pages8
Volume51
Edition9
DOIs
Publication statusPublished - 2018
Event15th IFAC Symposium on Control in Transportation Systems - Savona, Italy
Duration: 6 Jun 20188 Jun 2018
Conference number: 15
http://www.cts2018.unige.it/

Publication series

NameIFAC-PapersOnLine
PublisherElsevier
Number9
Volume51
ISSN (Print)2405-8963

Conference

Conference15th IFAC Symposium on Control in Transportation Systems
Abbreviated titleCTS 2018
CountryItaly
CitySavona
Period6/06/188/06/18
Internet address

Keywords

  • Catenary
  • Railway Systems
  • Multi-Objective Performance Evaluation
  • Deep convolutional neural networks (DCNNs

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    Liu, W., Liu, Z., Nunez, A., Wang, L., Liu, K., Lyu, Y., & Wang, H. (2018). Multi-Objective Performance Evaluation of the Detection of Catenary Support Components Using DCNNs. In B. De Schutter , & A. Ferrara (Eds.), Proceedings of 15th IFAC Symposium on Control in Transportation Systems (CTS 2018): Savona, Italy, June 6-8, 2018 (9 ed., Vol. 51, pp. 98-105). (IFAC-PapersOnLine; Vol. 51, No. 9). https://doi.org/10.1016/j.ifacol.2018.07.017