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http://dx.doi.org/10.7746/jkros.2017.12.4.425

Convolutional Neural Network-based Real-Time Drone Detection Algorithm  

Lee, Dong-Hyun (Electrical Engineering, Kumoh National Institute of Technology)
Publication Information
The Journal of Korea Robotics Society / v.12, no.4, 2017 , pp. 425-431 More about this Journal
Abstract
As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.
Keywords
Convolutional Neural Networks; Drone Detection;
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