Flying Object Detection and Classification by Monitoring Using Video Images

Sobue, Hideaki; Fukushima, Yuki; Kashiyama, Takahiro; Sekimoto, Yoshihide

doi:10.1145/3139958.3140026

Cited by 2 publications

(2 citation statements)

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“…Methods employing images [ 8 , 9 , 10 , 11 ] have likewise been proposed, as we can see from the fact that image processing is used in various fields [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. These methods have gained superiority, as they do not affect the monitoring targets or the surrounding environment and allow for systems to be built at a comparatively lower cost.…”

Section: Introductionmentioning

confidence: 99%

“…Another benefit is that it becomes possible to use an indoor monitoring system to monitor an outdoor environment, reducing maintenance costs. Sobue et al [ 8 ] and Seidaliyeva et al [ 11 ] used background differencing to detect flying objects. Schumann et al [ 9 ] used either background differencing or a deep neural network (DNN) to detect flying objects and used a separate convolutional neural network (CNN) model for categorization.…”

Section: Introductionmentioning

confidence: 99%

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Sky Monitoring System for Flying Object Detection Using 4K Resolution Camera

Kashiyama

Sobue

Sekimoto

2020

Sensors

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The use of drones and other unmanned aerial vehicles has expanded rapidly in recent years. These devices are expected to enter practical use in various fields, such as taking measurements through aerial photography and transporting small and lightweight objects. Simultaneously, concerns over these devices being misused for terrorism or other criminal activities have increased. In response, several sensor systems have been developed to monitor drone flights. In particular, with the recent progress of deep neural network technology, the monitoring of systems using image processing has been proposed. This study developed a monitoring system for flying objects using a 4K camera and a state-of-the-art convolutional neural network model to achieve real-time processing. We installed a monitoring system in a high-rise building in an urban area during this study and evaluated the precision with which it could detect flying objects at different distances under different weather conditions. The results obtained provide important information for determining the accuracy of monitoring systems with image processing in practice.

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