SyNet: An Ensemble Network for Object Detection in UAV Images

Albaba, Berat Mert; Özer, Sedat

doi:10.48550/arxiv.2012.12991

Cited by 1 publication

(2 citation statements)

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“…2020 -GDF-Net [61] Scale variation VisDrone, UAVDT Remote Sens. 2020 -HRDNet [70] Scale variation VisDrone CVPR 2020 -D-A-FS SSD [63] [63] Scale variation VisDrone ICIT 2020 -UAV-YOLO [16] Small scale UAV123, Own Sensors 2020 -SyNet [71] Class imbalance VisDrone ICPR 2020 https://github.com/mertalbaba/SyNet ComNet [72] Blurred edges, low contrast Own IEEE TGRS 2020 -MPFPN [73] Small objects VisDrone IEEE Access 2020 -D2Det [74],…”

Section: Object Detection On Small Objectsmentioning

confidence: 99%

“…Besides the aforementioned main challenges, other problems in object detection in UAV images are addressed, such as Nuisance Disentangled Feature Transform (NDFT) [69] for a large number of fine-grained domains, D2Det [74] for precise localization and accurate classification, combinational neural network (ComNet) [72] for blurred edges and low contrast, an ensemble network (SyNet) [71] for class imbalance problem and the scaling problem, and Dual Sampler and Head detection Network (DSHNet) for long-tail distribution.…”

Section: F Object Detection On Othersmentioning

confidence: 99%

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Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE Xplore. Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

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