LR-CNN : Local-aware Region CNN for vehicle detection in aerial imagery

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Liao, W.; Chen, X.; Yang, J.; Roth, S.; Goesele, M. et al.: LR-CNN : Local-aware Region CNN for vehicle detection in aerial imagery. In: Paparoditis, N. (Eds.): XXIV ISPRS Congress, Commission II : edition 2020. Katlenburg-Lindau : Copernicus Publications, 2020 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 5,2), S. 381-388. DOI:

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Sum total of downloads: 65

State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs' features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset. © 2020 Copernicus GmbH. All rights reserved.
License of this version: CC BY 4.0 Unported
Document Type: BookPart
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Elektrotechnik und Informatik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 27 41.54%
2 image of flag of United States United States 14 21.54%
3 image of flag of China China 6 9.23%
4 image of flag of Sweden Sweden 4 6.15%
5 image of flag of No geo information available No geo information available 3 4.62%
6 image of flag of Russian Federation Russian Federation 2 3.08%
7 image of flag of Netherlands Netherlands 2 3.08%
8 image of flag of Afghanistan Afghanistan 2 3.08%
9 image of flag of Saudi Arabia Saudi Arabia 1 1.54%
10 image of flag of Korea, Republic of Korea, Republic of 1 1.54%
    other countries 3 4.62%

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