Scipediacontent: Scipediacontent moved page Draft Content 614274104 to Azimi et al 2018a

2020-10-14T13:55:22Z

Scipediacontent moved page Draft Content 614274104 to Azimi et al 2018a

Scipediacontent: Created page with " == Abstract == Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic moni..."

2020-10-14T13:55:19Z

Created page with " == Abstract == Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic moni..."

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== Abstract ==

Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin (+6% and +12% absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_614274104-beopen896-1764-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1807.02700 http://arxiv.org/abs/1807.02700]

* [http://dx.doi.org/10.1007/978-3-030-20893-6_10 http://dx.doi.org/10.1007/978-3-030-20893-6_10]

* [https://elib.dlr.de/123599/1/Azimi_MF-PBA_ACCV_Dec2018_Perth_Australia.pdf https://elib.dlr.de/123599/1/Azimi_MF-PBA_ACCV_Dec2018_Perth_Australia.pdf]

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Azimi et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 614274104 to Azimi et al 2018a

Scipediacontent: Created page with " == Abstract == Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic moni..."