Deep Learning for Vehicle Detection in Aerial Images

M.Y. Yang, Wentong Liao, Xinbo Li, Bodo Rosenhahn

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)

Abstract

The detection of vehicles in aerial images is widely applied in many domains. In this paper, we propose a novel double focal loss convolutional neural network framework (DFL-CNN). In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.
Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Subtitle of host publication7-10 October 2018, Athens, Greece
PublisherIEEE
Pages3079-3083
ISBN (Electronic)978-1-4799-7061-2
DOIs
Publication statusPublished - Oct 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018
Conference number: 25
https://2018.ieeeicip.org/

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
Abbreviated titleICIP
CountryGreece
CityAthens
Period7/10/1810/10/18
Internet address

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  • Cite this

    Yang, M. Y., Liao, W., Li, X., & Rosenhahn, B. (2018). Deep Learning for Vehicle Detection in Aerial Images. In 2018 25th IEEE International Conference on Image Processing (ICIP): 7-10 October 2018, Athens, Greece (pp. 3079-3083). IEEE. https://doi.org/10.1109/ICIP.2018.8451454