Drone-surveillance for search and rescue in natural disaster
Drone-surveillance for search and rescue in natural disaster
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DOI：
10.1016/j.comcom.2020.03.012
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发表时间：
2020-04-15
影响因子：
6
通讯作者：
Mishra, Vipul
中科院分区：
文献类型：
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作者：
Mishra, Balmukund;Garg, Deepak;Mishra, Vipul
由于无人机的能力不断提高以及对偏远地区的监控要求，无人机监控变得越来越流行。在自然灾害发生时，它可以快速扫描大范围的受灾区域，使搜救（SAR）更快，以挽救更多的生命。然而，使用自主无人机进行搜索和救援的探索最少，需要研究人员的注意力来开发自主无人机监视的有效算法。要使用深度学习的最新进展开发自动化应用程序，数据集是关键。为此，需要大量的人体检测和动作检测数据集来训练深度学习模型。针对SAR无人机监控数据集缺乏的问题，提出了一种SAR人体动作检测图像数据集。建议的数据集包含从75，000张图像中筛选出的2000张独特图像。它包含了30000个不同动作的人类实例。此外，在本文中，各种实验进行建议的数据集，公开可用的数据集，和最先进的检测方法。我们的实验表明，现有的模型是不足够的关键应用，如SAR，这促使我们提出一个模型，它的灵感来自金字塔的SSD特征提取的人体检测和动作识别提出的模型达到0.98mAP时，应用于建议的数据集，这是一个显着的贡献。此外，与文献中最先进的检测模型相比，该模型在应用于标准Okutama数据集时，mAP值高出7%。
Due to the increasing capability of drones and requirements to monitor remote areas, drone surveillance is becoming popular. In case of natural disaster, it can scan the wide affected-area quickly and make the search and rescue (SAR) faster to save more human lives. However, using autonomous drone for search and rescue is least explored and require attention of researchers to develop efficient algorithms in autonomous drone surveillance. To develop an automated application using recent advancement of deep-learning, dataset is the key. For this, a substantial amount of human detection and action detection dataset is required to train the deep-learning models. As dataset of drone surveillance in SAR is not available in literature, this paper proposes an image dataset for human action detection for SAR. Proposed dataset contains 2000 unique images filtered from 75,000 images. It contains 30000 human instances of different actions. Also, in this paper various experiments are conducted with proposed dataset, publicly available dataset, and stat-of-the art detection method. Our experiments shows that existing models are not adequate for critical applications such as SAR, and that motivates us to propose a model which is inspired by the pyramidal feature extraction of SSD for human detection and action recognition Proposed model achieves 0.98mAP when applied on proposed dataset which is a significant contribution. In addition, proposed model achieve 7% higher mAP value when applied to standard Okutama dataset in comparison with the state-of-the-art detection models in literature.