Real-Time Rail Safety: A Deep Convolutional Neural Network Approach for Obstacle Detection on Tracks-Reference-Cited by-同舟云学术

Real-Time Rail Safety: A Deep Convolutional Neural Network Approach for Obstacle Detection on Tracks

Published:2023-03-23 Issue: Volume: Page:
ISSN:
Container-title:2023 4th International Conference on Signal Processing and Communication (ICSPC)
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Author:

Jenefa A¹,Ande Aaron¹,Mounikuttan Thejas¹,Anuj M.D.¹,Jenulin Makros G¹,Rejoice G Rachel¹,Shalini T Mary¹

Affiliation:

1. Karunya Institute of Tech,Dept. of CSE,Coimbatore,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10125570/10125282/10125284.pdf?arnumber=10125284

Reference15 articles.

1. Intrusion detection for high-speed railway perimeter obstacle;qinghong;In Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT),2017

2. Detection of safe passage for trains at rail level crossings using deep learning;pamu?a;SENSORS,2021

3. Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review;samira;Remote Sensing,2022

4. Multi-Objective Evolutionary for Object Detection Mobile Architectures Search;haichao;arXiv preprint arXiv 2211 02791,2022

5. A multiscale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter;chuan;Measurement 185,2021

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