SENSOR FUSION TECHNIQUES FOR DRONES IN IOT-BASED SURVEILLANCE SYSTEMS

Dinesh Kumar Reddy Basani; Basava Ramanjaneyulu Gudivaka; Raj Kumar Gudivaka; Rajya Lakshmi Gudivaka; Sri Harsha Grandhi; Aravindhan Kurunthachalam

doi:10.70454/IJMRE.2022.20501

Authors

Dinesh Kumar Reddy Basani CGI, British Columbia, Canada Author
Basava Ramanjaneyulu Gudivaka JP Morgan Services India Private Limited, Telangana, India Author
Raj Kumar Gudivaka Birlasoft Limited, Telangana, India. Author
Rajya Lakshmi Gudivaka Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India. Author
Sri Harsha Grandhi Intel, Folsom, California, USA Author
Aravindhan Kuruntha chalam Assistant professor SNS College of Technology, Coimbatore, Tamil Nadu, India. Author

DOI:

https://doi.org/10.70454/IJMRE.2022.20501

Keywords:

Internet of Things, Deep Learning methods, Convolutional Neural Network, Surveillance systems, Landslide Detection, Sensor Fusion

Abstract

This work introduces a novel system incorporating Internet of Things (IoT) empowered drones, robotics, and deep learning methods, specifically Convolutional Neural Networks (CNN), to identify areas affected by landslides. The system uses modern sensors including cameras, LiDAR scanners, and environmental sensors, offering detailed information from distant and dangerous environments. The CNN model analyses this information to classify areas as damaged or undamaged with very high precision. Robustness of the system is emphasized using multi-sensor data acquisition, efficient preprocessing, and application of transfer learning to deliver optimal performance. Results reflect a high classification accuracy of over 99%, validating the system's capability in real-time landslide detection and disaster management. The current work seeks to contribute to more scalable and efficient disaster management systems using IoT and AI technologies.

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