Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches
Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in unde...
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doaj-59e88af9fb5b40a59211bd39840034072020-11-25T03:08:39ZengMDPI AGElectronics2079-92922020-03-01958358310.3390/electronics9040583Detecting Objects from Space: An Evaluation of Deep-Learning Modern ApproachesKhang Nguyen0Nhut T. Huynh1Phat C. Nguyen2Khanh-Duy Nguyen3Nguyen D. Vo4Tam V. Nguyen5Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, VietnamDepartment of Software Engineering, University of Information Technology, Ho Chi Minh City 700000, VietnamDepartment of Software Engineering, University of Information Technology, Ho Chi Minh City 700000, VietnamMultimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, VietnamMultimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, VietnamDepartment of Computer Science, University of Dayton, Dayton, OH 45469, USAUnmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images.https://www.mdpi.com/2079-9292/9/4/583object detectionVisDrone2019aerial imageryFaster R-CNNSSDRFCN |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Khang Nguyen Nhut T. Huynh Phat C. Nguyen Khanh-Duy Nguyen Nguyen D. Vo Tam V. Nguyen |
spellingShingle |
Khang Nguyen Nhut T. Huynh Phat C. Nguyen Khanh-Duy Nguyen Nguyen D. Vo Tam V. Nguyen Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches Electronics object detection VisDrone2019 aerial imagery Faster R-CNN SSD RFCN |
author_facet |
Khang Nguyen Nhut T. Huynh Phat C. Nguyen Khanh-Duy Nguyen Nguyen D. Vo Tam V. Nguyen |
author_sort |
Khang Nguyen |
title |
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches |
title_short |
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches |
title_full |
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches |
title_fullStr |
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches |
title_full_unstemmed |
Detecting Objects from Space: An Evaluation of Deep-Learning Modern Approaches |
title_sort |
detecting objects from space: an evaluation of deep-learning modern approaches |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-03-01 |
description |
Unmanned aircraft systems or drones enable us to record or capture many scenes from the bird’s-eye view and they have been fast deployed to a wide range of practical domains, i.e., agriculture, aerial photography, fast delivery and surveillance. Object detection task is one of the core steps in understanding videos collected from the drones. However, this task is very challenging due to the unconstrained viewpoints and low resolution of captured videos. While deep-learning modern object detectors have recently achieved great success in general benchmarks, i.e., PASCAL-VOC and MS-COCO, the robustness of these detectors on aerial images captured by drones is not well studied. In this paper, we present an evaluation of state-of-the-art deep-learning detectors including Faster R-CNN (Faster Regional CNN), RFCN (Region-based Fully Convolutional Networks), SNIPER (Scale Normalization for Image Pyramids with Efficient Resampling), Single-Shot Detector (SSD), YOLO (You Only Look Once), RetinaNet, and CenterNet for the object detection in videos captured by drones. We conduct experiments on VisDrone2019 dataset which contains 96 videos with 39,988 annotated frames and provide insights into efficient object detectors for aerial images. |
topic |
object detection VisDrone2019 aerial imagery Faster R-CNN SSD RFCN |
url |
https://www.mdpi.com/2079-9292/9/4/583 |
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