CNN and Binocular Vision-Based Target Detection and Ranging Framework of Intelligent Railway System-Reference-Cited by-同舟云学术

CNN and Binocular Vision-Based Target Detection and Ranging Framework of Intelligent Railway System

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Liu Yuxi¹^ORCID,Wu Yanliang¹^ORCID,Ma Zheng¹^ORCID,Zhou Yi¹^ORCID,Li Guoqiang²^ORCID,Jian Xia³^ORCID,Meng Shijin¹^ORCID

Affiliation:

1. Provincial Key Laboratory of Information Coding and Transmission, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

2. Jinan to Laiwu High-Speed Railway Project Department, Jinan, China

3. State Key Laboratory of Rail Transit Engineering Informatization, China Railway First Survey and Design Institute Group Company Ltd., Xi’an, China

Funder

NSFC Project

Young Elite Scientists Sponsorship Program

China Association for Science and Technology

Research and Development Project of China Railway Wuhan Electrification Engineering Group Company Ltd

State Key Laboratory of Rail Transit Engineering Informatization

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10494548.pdf?arnumber=10494548

Reference38 articles.

1. Rise and future development of Chinese high-speed railway;Xiong;J. Traffic Transp. Eng.,2021

2. A Lightweight Framework for Obstacle Detection in the Railway Image Based on Fast Region Proposal and Improved YOLO-Tiny Network

3. An Unsupervised Fault-Detection Method for Railway Turnouts

4. MtlrNet: An Effective Deep Multitask Learning Architecture for Rail Crack Detection

5. Classification of Trackside Equipment Based on Convolutional Neural Network

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