Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology-Reference-Cited by-同舟云学术

Enhancing Facility Safety for Autonomous Gas Inspection Drones Leveraging Convolutional Neural Networks and loT Technology

Published:2024-03-14 Issue: Volume:11 Page:1-6
ISSN:
Container-title:2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)
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Author:

Latha S.¹,Asha P.²,Srinivasan V. Prasanna³,Elangovan K.⁴,R Thamizhamuthu⁵,Sujatha S.⁶

Affiliation:

1. Vinayaka Mission's Kirupananda Variyar Engineering College, Vinayaka Mission's Research Foundation(DU),Department of Biotechnology,Salem,Tamil Nadu,India

2. Sathyabama Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,Tamil Nadu,India

3. R.M.D. Engineering College,Department of Information Technology,Chennai,Tamil Nadu,India

4. Dhanalakshmi Srinivasan Engineering College,Department of Mechanical Engineering,Chennai,Tamil Nadu,India

5. SRM Institute of Science and Technology,Department of Computing Technologies,Chennai,Tamil Nadu,India

6. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University,Department of Biomedical Engineering,Chennai,Tamil Nadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10522096/10522101/10522305.pdf?arnumber=10522305

Reference18 articles.

1. Inspection of vertical structures in oil and gas industry: A review of current scenario and future trends

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3. Real-time Monitoring of Womens Safety Through Bluetooth Low Energy and iBeacons;Vivek

4. Intelligent Baggage Management in Airports: A Cognitive IoT Approach for Real-Time Tracking, Optimization, and Passenger Engagement

5. Energy Monitoring in Solar-Powered Buildings Using Internet of Things