A Comparative Assessment of Deep Neural Network Models for Detecting Obstacles in the Real Time Aerial Railway Track Images-Reference-Cited by-同舟云学术

A Comparative Assessment of Deep Neural Network Models for Detecting Obstacles in the Real Time Aerial Railway Track Images

Published:2022-01-04 Issue:1 Volume:36 Page:
ISSN:0883-9514
Container-title:Applied Artificial Intelligence
language:en
Short-container-title:Applied Artificial Intelligence

Author:

Rampriya R. S.¹^ORCID,Suganya R.²,Nathan Sabari³,Perumal P. Shunmuga⁴

Affiliation:

1. Department of Computer Technology, Anna University (MIT Campus), Chennai, India

2. Information Technology, Thiagarajar College of Engineering, Madurai, India

3. Couger Inc, Tokyo, Japan

4. Automotive Research Center, VIT University, Vellore, India

Publisher

Informa UK Limited

Subject

Artificial Intelligence

Link

https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2018184

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2. Machine learning crash course - Google’s best practices on splitting data. 2020. Available online: https://tinyurl.com/y7yqfhxu (accessed February 28, 2021).

3. Indian Railways procures Ninja drones for security surveillance and passenger safety 2021. Available Online: https://economictimes.indiatimes.com/industry/transportation/railways/indian-railways-introduces-drone-based-surveillance-system-for-rail-security/articleshow/77621214.cms?from=mdr (accessed February 10, 2021).

4. A Fast Video Image Detection using TensorFlow Mobile Networks for Racing Cars

5. Breton, M. 2019. Overview of two performance metrics for object detection algorithms evaluation. Defence Research and Development Canada. DRDC-RDDC-2019-D168.

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