Real-Time Disaster Classification Using UAV Imagery and Efficient Net for Resource-Constrained Environments

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Bollu Jagadeesh; Mr.K. Mohan Krishna; Dr. Ramachandran Vedantam

Real-Time Disaster Classification Using UAV Imagery and Efficient Net for Resource-Constrained Environments

Authors: Bollu Jagadeesh, Mr.K. Mohan Krishna, Dr. Ramachandran Vedantam

Unique Paper ID: 170732
Volume: 11
Issue: 7
PageNo: 1458-1462

Keywords: Disaster Management UAV Imagery Efficient-Net Deep Learning Disaster Classification Real-Time Monitoring Resource-Constrained Systems Aerial Image Processing

Abstract:
Disaster management is essential for mitigating the effects of natural and human-made catastrophes on people and the environment. Rapid and precise assessment of disaster-affected areas is crucial for effective response and recovery efforts. Unmanned Aerial Vehicles (UAVs), equipped with high-resolution imaging systems, have proven to be indispensable for capturing real-time aerial views of disaster zones. However, efficiently processing these images for accurate disaster classification remains a significant challenge. This paper introduces an advanced disaster monitoring framework that utilizes Efficient-Net, a cutting-edge deep learning architecture, to classify UAV-acquired images based on different disaster scenarios. By fine-tuning a pretrained Efficient-Net model specifically for disaster detection, the system achieves remarkable accuracy in categorizing images into classes such as damaged infrastructure, fires, water-related disasters, human casualties, and non-critical conditions. The proposed solution is optimized to balance computational efficiency with real-time performance, making it suitable for deployment in environments with limited resources. Comprehensive evaluations on a disaster-specific image dataset demonstrate the model's superior accuracy and faster processing speeds compared to existing methods. This study emphasizes the potential of Efficient-Net to enhance UAV-based disaster monitoring systems by improving their scalability and real-time operational capabilities.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 1458-1462

Real-Time Disaster Classification Using UAV Imagery and Efficient Net for Resource-Constrained Environments

Available:https://ijirt.org/article?manuscript=170732

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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