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Enhanced Bird Detection from Low-Resolution Aerial Image Using Deep Neural Networks

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Abstract

Bird detection in LR images is essential for the applications of unmanned aerial vehicles. It is still a challenging task because traditional discriminative features in high-resolution (HR) usually disappear in low-resolution (LR) images. Although recent advances in single image super-resolution (SISR) and object detection algorithms have offered unprecedented potential for computer-automated reconstructing LR images and detecting various objects, these algorithms are mainly evaluated using synthetic datasets. It is unclear how these algorithms would perform on bird images acquired in the wild and how we could gauge the progress in the real-time bird detection. This paper presents a novel bird detection framework in LR aerial images using deep neural networks (DNN). We collect a dataset named BIRD-50 and a public dataset named CUB-200 of real bird images with different scale low-resolutions. Using these datasets, we introduce a novel DNN based framework for bird detection in reconstructed HR images, which exploits the mapping function from LR to HR aerial image and detects the birds by the state-of-the-art object feature extraction and localization methods. By systematically analyzing the influence of the resolution reduction on the bird detection, the experimental results indicate that our approach has produced significantly improved detection precision for bird detection by the inclusion of SISR algorithms.

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Notes

  1. BIRD-50 will be avaliable at the website: https://github.com/bczhang/bczhang/.

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Acknowledgements

The work was supported by the Natural Science Foundation of China under Contract 61601466, 61672079 and 61473086, and Shenzhen Peacock Plan KQTD201611 2515134654. This work was also supported by the Open Projects Program of National Laboratory of Pattern Recognition. Ce Li and Baochang Zhang are the correspondence authors.

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Authors and Affiliations

  1. Department of Computer Science, China University of Mining and Technology, Beijing, China

    Ce Li

  2. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Baochang Zhang & Hanwen Hu

  3. State Key Laboratory of Satellite Navigation System and Equipment Techonology, Shijiazhuang, Hebei, China

    Baochang Zhang

  4. Shenzhen Academy of Aerospace Technology, Shenzhen, China

    Baochang Zhang

  5. China Academy of Launch Vehicle Technology R&D Center, Beijing, China

    Jing Dai

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  1. Ce Li
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  2. Baochang Zhang
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  3. Hanwen Hu
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  4. Jing Dai
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Correspondence to Baochang Zhang.

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Li, C., Zhang, B., Hu, H. et al. Enhanced Bird Detection from Low-Resolution Aerial Image Using Deep Neural Networks. Neural Process Lett 49, 1021–1039 (2019). https://doi.org/10.1007/s11063-018-9871-z

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