Abstract
The aerial target detection and recognition are very challenging due to large appearance, lighting and orientation variations. We propose a Deep-patch Orientation Network (DON) method, which is general and can learn the encoded orientation information based on any off the-shelf deep detection framework, e.g., Faster-RCNN and YOLO, and result into higher performance in airplane target detection and classification tasks. Most existing methods neglected the orientation information, which in DON is obtained based on the structure information contained in the patch training samples. In testing process, we introduce an orientation based method to exploit patches for whole target localization. Also, we analyzed how to improve agnostic-target detection framework by tailoring the reference boxes. Experimental results on two datasets show that, our proposed DON method improves the recall at high precision rates for the deep detection framework and provide orientation information for detected targets.
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Acknowledgments
The work was supported in part by the Natural Science Foundation of China under Contract 61672079, 61473086 and 61601466. The work of B. Zhang was supported in part by the Program for New Century Excellent Talents University within the Ministry of Education, China, and in part by the Beijing Municipal Science and Technology Commission under Grant Z161100001616005.
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Maher, A., Gu, J., Zhang, B. (2018). Deep-Patch Orientation Network for Aircraft Detection in Aerial Images. In: Wang, Y., et al. Advances in Image and Graphics Technologies. IGTA 2017. Communications in Computer and Information Science, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-10-7389-2_18
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DOI: https://doi.org/10.1007/978-981-10-7389-2_18
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