In this paper, we propose a novel crowd detection method for drone safe landing, based on an extremely light and fast fully convolutional neural network. Such a computer vision application takes advantage of the technical tools some commercial drones are equipped with. The proposed architecture is based on a two-loss model in which the main classification task, aimed at distinguishing between crowded and non-crowded scenes, is simultaneously assisted by a regression task, aimed at people counting. In addition, the proposed method provides class activation heatmaps, useful to semantically augment the flight maps. To evaluate the effectiveness of the proposed approach, we used the challenging VisDrone dataset, characterized by a very large variety of locations, environments, lighting conditions, and so on. The model developed by the proposed two-loss deep architecture achieves good values of prediction accuracy and average precision, outperforming models developed by a similar one-loss architecture and a more classic scheme based on MobileNet. Moreover, by lowering the confidence threshold, the network achieves very high recall, without sacrificing too much precision. The method also compares favorably with the state-of-the-art, providing an effective and efficient tool for several safe drone applications.

Crowd Detection for Drone Safe Landing Through Fully-Convolutional Neural Networks

Castellano, Giovanna;Castiello, Ciro;Mencar, Corrado;Vessio, Gennaro
2020

Abstract

In this paper, we propose a novel crowd detection method for drone safe landing, based on an extremely light and fast fully convolutional neural network. Such a computer vision application takes advantage of the technical tools some commercial drones are equipped with. The proposed architecture is based on a two-loss model in which the main classification task, aimed at distinguishing between crowded and non-crowded scenes, is simultaneously assisted by a regression task, aimed at people counting. In addition, the proposed method provides class activation heatmaps, useful to semantically augment the flight maps. To evaluate the effectiveness of the proposed approach, we used the challenging VisDrone dataset, characterized by a very large variety of locations, environments, lighting conditions, and so on. The model developed by the proposed two-loss deep architecture achieves good values of prediction accuracy and average precision, outperforming models developed by a similar one-loss architecture and a more classic scheme based on MobileNet. Moreover, by lowering the confidence threshold, the network achieves very high recall, without sacrificing too much precision. The method also compares favorably with the state-of-the-art, providing an effective and efficient tool for several safe drone applications.
978-3-030-38918-5
978-3-030-38919-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/255857
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