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# Object Detection In Aerial Images Edit

8 papers with code · Computer Vision

Object Detection in Aerial Images is the task of detecting objects from aerial images.

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# Gliding vertex on the horizontal bounding box for multi-oriented object detection

21 Nov 2019xuannianz/EfficientDet

Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.

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# DOTA: A Large-Scale Dataset for Object Detection in Aerial Images

The fully annotated DOTA images contains 188, 282 instances, each of which is labeled by an arbitrary (8 d. o. f.)

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# DOTA: A Large-scale Dataset for Object Detection in Aerial Images

The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)

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# Learning RoI Transformer for Oriented Object Detection in Aerial Images

Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects.

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# SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.

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# Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

1 Dec 2018dingjiansw101/RoITransformer_DOTA

Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects.

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# Clustered Object Detection in Aerial Images

The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet).

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# DroNet: Efficient convolutional neural network detector for real-time UAV applications

18 Jul 2018gplast/DroNet

Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%.

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