Object Detection in Aerial Images Using Feature Fusion Deep Networks

Hao Long, Yi-Nung Chung, Zhenbao Liu, Shuhui Bu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.

Original languageEnglish
Article number8661761
Pages (from-to)30980-30990
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Fusion reactions
Antennas
Urban planning
Agriculture
Neural networks
Object detection
Deep neural networks

All Science Journal Classification (ASJC) codes

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

Cite this

Long, Hao ; Chung, Yi-Nung ; Liu, Zhenbao ; Bu, Shuhui. / Object Detection in Aerial Images Using Feature Fusion Deep Networks. In: IEEE Access. 2019 ; Vol. 7. pp. 30980-30990.
@article{f61e666a6a164202b6985e7595ed1eb9,
title = "Object Detection in Aerial Images Using Feature Fusion Deep Networks",
abstract = "Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.",
author = "Hao Long and Yi-Nung Chung and Zhenbao Liu and Shuhui Bu",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/ACCESS.2019.2903422",
language = "English",
volume = "7",
pages = "30980--30990",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Object Detection in Aerial Images Using Feature Fusion Deep Networks. / Long, Hao; Chung, Yi-Nung; Liu, Zhenbao; Bu, Shuhui.

In: IEEE Access, Vol. 7, 8661761, 01.01.2019, p. 30980-30990.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Object Detection in Aerial Images Using Feature Fusion Deep Networks

AU - Long, Hao

AU - Chung, Yi-Nung

AU - Liu, Zhenbao

AU - Bu, Shuhui

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.

AB - Object detection acts as an essential part in a wide range of measurement systems in traffic management, urban planning, defense, agriculture, and so on. Convolutional Neural Networks-based researches reach a great improvement on detection tasks in natural scene images enjoying from the strong ability of feature representations. However, because of the high density, the small size of objects, and the intricate background, the current methods achieve relatively low precision in aerial images. The intention of this work is to obtain better detection performance in aerial images by designing a novel deep neural network framework called Feature Fusion Deep Networks (FFDN). The novel architecture combines a designed structural learning layer based on a graphical model. As a result, the network not only provides powerful hierarchical representation but also strengthens the spatial relationship between the high-density objects. We demonstrate the great improvement of the proposed FFDN on the UAV123 data set and another novel challenging data set called UAVDT benchmark. The objects which appear with small size, partial occlusion and out of view, as well as in the dark background can be detected accurately.

UR - http://www.scopus.com/inward/record.url?scp=85064651668&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85064651668&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2903422

DO - 10.1109/ACCESS.2019.2903422

M3 - Article

AN - SCOPUS:85064651668

VL - 7

SP - 30980

EP - 30990

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

M1 - 8661761

ER -