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http://dx.doi.org/10.3837/tiis.2020.12.010

Deeper SSD: Simultaneous Up-sampling and Down-sampling for Drone Detection  

Sun, Han (College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics)
Geng, Wen (College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics)
Shen, Jiaquan (College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics)
Liu, Ningzhong (College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics)
Liang, Dong (College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics)
Zhou, Huiyu (School of Informatics, University of Leicester)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.12, 2020 , pp. 4795-4815 More about this Journal
Abstract
Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.
Keywords
Drone Detection; Small Object Detection; Deeper SSD; Up-sampling Modules; Down-sampling Modules;
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