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From simulation to reality: ground vehicle detection in aerial imagery based on deep learning
Author(s): Yu Yang; Chengpo Mu; Ruiheng Zhang; Xuejian Li; Ruixin Yang
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Paper Abstract

Collecting aerial data from physical world is usually time-consuming. Image simulation is a significant data source for various ground target detection systems. Unfortunately, the reality gap between simulated and real data makes the model trained on simulated image not workable on real image. A translation method is proposed for tackling the simulation-toreality problem in this paper. First, image simulation system is employed for data preparation. Then, the simulated data is converted into a more similar one to the real image. The segmentation map is the bridge between simulated and real data. At last, the target detection model is used as the utility evaluation method for the simulated data. The simulated and synthesized data is used to train a vehicle detection model. Experiments show that results trained by synthesized data are really close to the real results. The proposed translation method can assist real image for target detection task, which is an effective data augmentation method for aerial data.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792I (14 August 2019); doi: 10.1117/12.2539755
Show Author Affiliations
Yu Yang, Beijing Institute of Technology (China)
Chengpo Mu, Beijing Institute of Technology (China)
Ruiheng Zhang, Beijing Institute of Technology (China)
Xuejian Li, Beijing Institute of Technology (China)
Ruixin Yang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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