Unsupervised Cluster Guided Object Detection in Aerial Images
Object detection from high-resolution aerial images has received increasing attention during the last few years. It is a common practice to downsize images before feeding them into a network. In real life, there are lots of scenes where many objects gather together in certain areas, such as crossroa...
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IEEE
2021
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oai:doaj.org-article:dbad4eb4dcbb43899a62e37730f387b92021-11-17T00:00:15ZUnsupervised Cluster Guided Object Detection in Aerial Images2151-153510.1109/JSTARS.2021.3122152https://doaj.org/article/dbad4eb4dcbb43899a62e37730f387b92021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585637/https://doaj.org/toc/2151-1535Object detection from high-resolution aerial images has received increasing attention during the last few years. It is a common practice to downsize images before feeding them into a network. In real life, there are lots of scenes where many objects gather together in certain areas, such as crossroads, parking lots, and playgrounds. The downsizing operation significantly limits the detection ability in these scenes. In this article, we proposed an unsupervised cluster guided detection framework (UCGNet) to address these issues by guiding the detector focus on the object-densely distributed area. In particular, a local location module is first applied to predict a binary map presenting how objects distribute in terms of the pixel of the map. Then, an unsupervised cluster method is used to produce dense regions. Each adjusted dense region is fed into the detector for object detection. Finally, a global merge module generates the final predict results. Experiments were conducted on two popular aerial image datasets including VisDrone2019 and UAVDT. In both datasets, our proposed method outperforms the existing baseline methods with achieving 32.8% and 19.1% mAP, respectively.Jiajia LiaoYingchao PiaoJinhe SuGuorong CaiXingwang HuangLong ChenZhaohong HuangYundong WuIEEEarticleAerial imageconvolutional neural networks (CNNs)deep learningobject detectionunmanned aerial vehiclesOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11204-11216 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Aerial image convolutional neural networks (CNNs) deep learning object detection unmanned aerial vehicles Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
| spellingShingle |
Aerial image convolutional neural networks (CNNs) deep learning object detection unmanned aerial vehicles Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Jiajia Liao Yingchao Piao Jinhe Su Guorong Cai Xingwang Huang Long Chen Zhaohong Huang Yundong Wu Unsupervised Cluster Guided Object Detection in Aerial Images |
| description |
Object detection from high-resolution aerial images has received increasing attention during the last few years. It is a common practice to downsize images before feeding them into a network. In real life, there are lots of scenes where many objects gather together in certain areas, such as crossroads, parking lots, and playgrounds. The downsizing operation significantly limits the detection ability in these scenes. In this article, we proposed an unsupervised cluster guided detection framework (UCGNet) to address these issues by guiding the detector focus on the object-densely distributed area. In particular, a local location module is first applied to predict a binary map presenting how objects distribute in terms of the pixel of the map. Then, an unsupervised cluster method is used to produce dense regions. Each adjusted dense region is fed into the detector for object detection. Finally, a global merge module generates the final predict results. Experiments were conducted on two popular aerial image datasets including VisDrone2019 and UAVDT. In both datasets, our proposed method outperforms the existing baseline methods with achieving 32.8% and 19.1% mAP, respectively. |
| format |
article |
| author |
Jiajia Liao Yingchao Piao Jinhe Su Guorong Cai Xingwang Huang Long Chen Zhaohong Huang Yundong Wu |
| author_facet |
Jiajia Liao Yingchao Piao Jinhe Su Guorong Cai Xingwang Huang Long Chen Zhaohong Huang Yundong Wu |
| author_sort |
Jiajia Liao |
| title |
Unsupervised Cluster Guided Object Detection in Aerial Images |
| title_short |
Unsupervised Cluster Guided Object Detection in Aerial Images |
| title_full |
Unsupervised Cluster Guided Object Detection in Aerial Images |
| title_fullStr |
Unsupervised Cluster Guided Object Detection in Aerial Images |
| title_full_unstemmed |
Unsupervised Cluster Guided Object Detection in Aerial Images |
| title_sort |
unsupervised cluster guided object detection in aerial images |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/dbad4eb4dcbb43899a62e37730f387b9 |
| work_keys_str_mv |
AT jiajialiao unsupervisedclusterguidedobjectdetectioninaerialimages AT yingchaopiao unsupervisedclusterguidedobjectdetectioninaerialimages AT jinhesu unsupervisedclusterguidedobjectdetectioninaerialimages AT guorongcai unsupervisedclusterguidedobjectdetectioninaerialimages AT xingwanghuang unsupervisedclusterguidedobjectdetectioninaerialimages AT longchen unsupervisedclusterguidedobjectdetectioninaerialimages AT zhaohonghuang unsupervisedclusterguidedobjectdetectioninaerialimages AT yundongwu unsupervisedclusterguidedobjectdetectioninaerialimages |
| _version_ |
1718426078919262208 |