SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images

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 hei...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-03, Vol.14 (6), p.1313
Main Authors: Liu, Fanfan, Zhao, Wenzhe, Zhou, Guangyao, Zhao, Liangjin, Wei, Haoran
Format: Article
Language:English
Subjects:
Ablation
Boxes
Classification
distance-regression-free
Methods
Neural networks
Object recognition
Periodicity
Regression
Remote sensing
remote sensing imagery
Representations
Salience
Sensors
vehicle detection
Vehicle detectors
Vehicles
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Summary: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 3K Munich, and VEDAI, respectively.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14061313