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

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journal contribution
posted on 27.11.2020, 10:15 by H Sun, W Geng, J Shen, N Liu, D Liang, Huiyu Zhou
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.

History

Citation

KSII Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4795-4815, 2020. DOI: 10.3837/tiis.2020.12.010.

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

KSII Transactions on Internet and Information Systems

Volume

14

Issue

12

Publisher

Korea Society of Internet Information

issn

1976-7277

Acceptance date

24/11/2020

Copyright date

2020

Available date

08/07/2021

Language

en