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

摘要/Abstract

摘要：

针对传统机场检测方法准确率低、虚警率高、耗时长等问题,借鉴深度卷积神经网络的架构，提出一种改进的区域提取网络和自适应池化网络结合的机场快速检测方法．将二分类网络引入区域提取网络以筛除一些定位较差的候选区域和背景区域，结合自适应池化的检测网络对机场候选区域进行识别，通过复用网络结构和学习的特征参数来达到快速检测的目的．仿真结果表明，与两种典型的机场检测方法相比，所提方法在测试集上取得更高准确率和更低虚警率的同时，极大地提高了检测速度，达到了精准、快速检测机场的目的．

关键词: 机场检测, 卷积神经网络, 区域提取网络, 自适应池化

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

辛鹏;许悦雷;马时平;李帅;吕超. 区域提取网络结合自适应池化网络的机场检测[J]. 西安电子科技大学学报, 2018, 45(3): 102-108.

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.

参考文献

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