UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model

TL;DR

This paper presents a UAV-assisted framework with an optimized transformer model for real-time disaster detection, utilizing a new UAV-captured dataset and post-training quantization to enable efficient onboard processing.

Contribution

The work introduces a novel UAV-based disaster detection system with an optimized transformer model and a new dataset, addressing hardware limitations and real-world deployment challenges.

Findings

High accuracy achieved on UAV data

Reduced inference latency and memory usage

Effective real-time detection on resource-limited devices

Abstract

Dangerous surroundings and difficult-to-reach landscapes introduce significant complications for adequate disaster management and recuperation. These problems can be solved by engaging unmanned aerial vehicles (UAVs) provided with embedded platforms and optical sensors. In this work, we focus on enabling onboard aerial image processing to ensure proper and real-time disaster detection. Such a setting usually causes challenges due to the limited hardware resources of UAVs. However, privacy, connectivity, and latency issues can be avoided. We suggest a UAV-assisted edge framework for disaster detection, leveraging our proposed model optimized for onboard real-time aerial image classification. The optimization of the model is achieved using post-training quantization techniques. To address the limited number of disaster cases in existing benchmark datasets and therefore ensure real-world adoption of our model, we construct a novel dataset, DisasterEye, featuring disaster scenes captured by UAVs and individuals on-site. Experimental results reveal the efficacy of our model, reaching high accuracy with lowered inference latency and memory use on both traditional machines and resource-limited devices. This shows that the scalability and adaptability of our method make it a powerful solution for real-time disaster management on resource-constrained UAV platforms.