Detection of Dead Victims at Volcanic Disaster Location based on Drone and LoRa

M. Z. S. Hadi, Achmad Abie Dafa, P. Kristalina
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引用次数: 2

Abstract

When a natural disaster occurs, the process of evacuating victims after a disaster must be carried out immediately to reduce the risk due to the late evacuation process. In this evacuation process, the Search and Rescue (SAR) team played a big role in addition to focusing on the safety of victims and also paying attention to their own safety. Meanwhile, due to the large area of the disaster the search took quite a long time. In this paper, research was carried out to facilitate the evacuation process at the location of volcanic disasters by using drones carrying electronic devices to find the whereabouts of victims on the surface. This process of detecting and classifying is based on the Convolutional Neural Network (CNN) method with the MobileNetV2 model. We train the data set of disaster victims using batch sizes 16 and 32 with epochs of 40, 80 and 100. The resulting model has the greatest accuracy of 0.81 and f1-score of 0.86.
基于无人机和LoRa的火山灾害定位遇难者识别
当自然灾害发生时,必须立即进行灾后疏散过程,以减少由于疏散过程较晚而带来的风险。在这次疏散过程中,搜救队除了关注受害者的安全,也关注他们自己的安全,发挥了很大的作用。同时,由于受灾面积大,搜救工作耗时较长。本文研究利用携带电子设备的无人机在地表寻找遇难者的下落,以方便火山灾害地点的疏散过程。该检测和分类过程基于卷积神经网络(CNN)方法和MobileNetV2模型。我们使用batch大小为16和32,epoch分别为40,80和100来训练灾难受害者的数据集。所得模型的最高准确率为0.81,f1-score为0.86。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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