Name
Abstract | ||
|---|---|---|
Vehicle detection from earth-observation (EO) image has been attracting remarkable attention for its critical value in a variety of applications. Encouraged by the stunning success of deep learning techniques based on convolutional neural networks (CNNs), which have revolutionized the visual data processing community and obtained the state-of-the-art performance in a variety of classification and recognition tasks on benchmark datasets, we propose a network, called EOVNet (EO image-based vehicle detection network), to bridge the gap between the advanced deep learning research of object detection and the specific task of vehicle detection in EO images. Our network has integrated nearly all advanced techniques including very deep residual networks for feature extraction, feature pyramid to fuse multiscale features, network for proposal generation with feature sharing, and hard example mining. Moreover, our novel designs of probability-based localization and homography-based data augmentation have been investigated, resulting in further improvement of the detection performance. For performance evaluation, we have collected nearly all the representative EO datasets associated with vehicle detection. Extensive experiments on the representative datasets demonstrate that our method outperforms the state-of-the-art object detection approach Faster R-CNN++ (which is based on the Faster R-CNN framework, but with significant improvement) with 5% average precision improvement. The source code will be made available after the review process. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTARS.2019.2933501 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Keywords | Field | DocType |
Feature extraction,Vehicle detection,Proposals,Object detection,Neural networks,Task analysis,Deep learning | Computer vision,Object detection,Data processing,Pattern recognition,Source code,Convolutional neural network,Feature extraction,Earth observation,Artificial intelligence,Deep learning,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 9 | 1939-1404 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Gao | 1 | 33 | 10.15 |
| Hong Ji | 2 | 815 | 78.32 |
| Tiancan Mei | 3 | 2 | 2.04 |
| Ramesh Bharath | 4 | 47 | 8.96 |
| Xiaodong Liu | 5 | 1 | 0.34 |