基于优化Transformer模型的无人机辅助实时灾害检测
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model
AI总结：
本研究提出一种基于优化Transformer模型的无人机辅助边缘框架，利用训练后量化技术实现机载实时灾害检测，并构建DisasterEye数据集，在资源受限设备上达到高准确率、低延迟和低内存使用。
摘要：
危险的环境和难以到达的地形给充分的灾害管理和恢复带来了显著的复杂性。这些问题可以通过使用配备嵌入式平台和光学传感器的无人机（UAV）来解决。在这项工作中，我们专注于实现机载空中图像处理，以确保正确且实时的灾害检测。由于无人机硬件资源有限，这种设置通常会导致挑战。然而，隐私、连接性和延迟问题可以被避免。我们提出了一种用于灾害检测的无人机辅助边缘框架，利用我们提出的针对机载实时空中图像分类进行优化的模型。该模型的优化通过训练后量化技术实现。为了解决现有基准数据集中灾害案例数量有限的问题，并因此确保我们模型在现实世界中的采用，我们构建了一个新颖的数据集DisasterEye，其中包含由无人机和现场个人捕获的灾害场景。实验结果表明了我们模型的有效性，在传统机器和资源受限设备上均达到了高准确率，同时降低了推理延迟和内存使用。这表明我们方法的可扩展性和适应性使其成为资源受限无人机平台上实时灾害管理的强大解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
