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- Title
SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images.
- Authors
Liu, Fanfan; Zhao, Wenzhe; Zhou, Guangyao; Zhao, Liangjin; Wei, Haoran
- Abstract
Vehicle detection in remote sensing imagery is a challenging task because of its inherent attributes, e.g., dense parking, small sizes, various angles, etc. Prevalent vehicle detectors adopt an oriented/rotated bounding box as a basic representation, which needs to apply a distance regression of height, width, and angles of objects. These distance-regression-based detectors suffer from two challenges: (1) the periodicity of the angle causes a discontinuity of regression values, and (2) small regression deviations may also cause objects to be missed. To this end, in this paper, we propose a new vehicle modeling strategy, i.e., regarding each vehicle-rotated bounding box as a saliency area. Based on the new representation, we propose SR-Net (saliency region representation network), which transforms the vehicle detection task into a saliency object detection task. The proposed SR-Net, running in a distance (e.g., height, width, and angle)-regression-free way, can generate more accurate detection results. Experiments show that SR-Net outperforms prevalent detectors on multiple benchmark datasets. Specifically, our model yields 52.30%, 62.44%, 68.25%, and 55.81% in terms of AP on DOTA, UCAS-AOD, DLR 3 K Munich, and VEDAI, respectively.
- Subjects
MUNICH (Germany); REMOTE sensing; VEHICLE detectors; LONG-distance running; OBJECT recognition (Computer vision); VEHICLE models; DETECTORS
- Publication
Remote Sensing, 2022, Vol 14, Issue 6, p1313
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs14061313