Abstract
In the past decade, great progress has been made in general object detection based on deep convolutional neural networks. However, object detection from Unmanned Aerial Vehicles (UAV) imagery received not so much concern. In this paper, a densely connected feature mining network is proposed for high accuracy detection. Specifically, multi-scale predictions are used to enhance the feature representation of the tiny vehicles. Furthermore, a streamlined one-stage detection network is used to achieve satisfactory trade-off between speed and accuracy. Finally, a improved distance metric function is integrated into the priors clustering process, which can lead to a better preliminary location before training. The proposed architecture is evaluated on the highly competitive UAV benchmark (UAVDT). The experimental results show that the proposed dense-darknet network has achieved a competitive performance of 42.03% mAP (mean Average Precision) and good generalization ability on the other UAV benchmarks.
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Acknowledgments
This work is supported by National Key Research and Development Plan under Grant No. 2016YFC0801005. This work is supported by Grant No. 2018JXYJ49.
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Li, J., Wang, R., Ding, J. (2019). Tiny Vehicle Detection from UAV Imagery. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_36
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DOI: https://doi.org/10.1007/978-981-13-9917-6_36
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