TIB-Net: Drone Detection Network With Tiny Iterative Backbone

Han Sun*, Jian Yang, Jiaquan Shen, Dong Liang, Liu Ning-Zhong, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
3 Downloads (Pure)

Abstract

With the widespread application of drone in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to various appearance of small-size drones, changeable and complex environments, and limited memory resources of edge computing devices, drone detection remains a challenging task nowadays. Although deep convolutional neural network (CNN) has shown powerful performance in object detection in recent years, most existing CNN-based methods cannot balance detection performance and model size well. To solve the problem, we develop a drone detection network with tiny iterative backbone named TIB-Net. In this network, we propose a structure called cyclic pathway, which enhances the capability to extract effective features of small object, and integrate it into existing efficient method Extremely Tiny Face Detector (EXTD). This method not only significantly improves the accuracy of drone detection, but also keeps the model size at an acceptable level. Furthermore, we integrate spatial attention module into our network backbone to emphasize information of small object, which can better locate small-size drone and further improve detection performance. In addition, we present massive manual annotations of object bounding boxes for our collected 2860 drone images as a drone benchmark dataset, which is now publicly available1. In this work, we conduct a series of experiments on our collected dataset to evaluate TIB-Net, and the result shows that our proposed method achieves mean average precision of 89.2% with model size of 697.0KB, which achieves the state-of-the-art results compared with existing methods.

Original languageEnglish
Article number9141228
Pages (from-to)130697-130707
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 15 Jul 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant NZ2019009.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • cyclic pathway
  • drone benchmark dataset
  • Drone detection
  • spatial attention
  • TIB-Net
  • tiny iterative backbone

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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