区域提取网络结合自适应池化网络的机场检测

doi:10.3969/j.issn.1001-2400.2018.03.019

Abstract

Abstract:

Traditional airport detection methods have a low accuracy, high false alarm rate and low efficiency. Motivated by the architecture of deep convolutional neural networks, we propose a fast airport detection method combining improved Region Proposal Networks and adaptive pooling networks. We add a cascade two-class classification network after the Region Proposal Networks to shrink the badly located proposals and background proposals, and adopt the adaptive pooling networks to recognize the optimized candidate regions. The method achieves the purpose of rapid detection by multiplexing the network structure and reusing the learned features of proposals from the same input image. Simulation results show that comparing with two typical methods, our method gets a higher detection rate, lower false-alarm rate and greatly reduces the detection time on the test dataset, which meets the requirements of accurate and fast detection.

Key words: airport detection, convolutional neural networks, region proposal networks, adaptive pooling

XIN Peng;XU Yuelei;MA Shiping;LI Shuai;LV Chao. Airport detection combining region proposal networks and adaptive pooling networks[J].Journal of Xidian University, 2018, 45(3): 102-108.

References

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Airport detection combining region proposal networks and adaptive pooling networks

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