| 基于优化Faster-RCNN的遥感影像飞机检测 |
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| 引用本文： | 林娜,冯丽蓉,张小青. 基于优化Faster-RCNN的遥感影像飞机检测[J]. 遥感技术与应用, 2021, 36(2): 275-284. DOI: 10.11873/j.issn.1004-0323.2021.2.0275 |
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| 作者姓名： | 林娜 冯丽蓉 张小青 |
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| 作者单位： | 1.重庆交通大学 土木工程学院，重庆 400074;2.重庆市地理信息和遥感应用中心，重庆 401147 |
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| 基金项目： | 重庆市教委科技项目(KJQN201800747);重庆交通大学研究生教育创新基金项目(2020S0001) |
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| 摘 要： | 针对传统飞机检测算法特征学习能力较弱,在背景复杂、目标密集、成像质量较差的遥感影像上检测精度较低的问题,提出了 一种基于Faster-RCNN(Faster-Regions with Convolutional Neural Network)框架的遥感影像飞机检测优化算法.以ResNet50为基础特征提取网络,引入空洞...
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| 关 键 词： | 深度学习 遥感影像 目标检测 特征融合 空洞残差块 |
| 收稿时间： | 2020-04-21 |
Aircraft Detection in Remote Sensing Image based on Optimized Faster-RCNN |
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| Na Lin,Lirong Feng,Xiaoqing Zhang. Aircraft Detection in Remote Sensing Image based on Optimized Faster-RCNN[J]. Remote Sensing Technology and Application, 2021, 36(2): 275-284. DOI: 10.11873/j.issn.1004-0323.2021.2.0275 |
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| Authors: | Na Lin Lirong Feng Xiaoqing Zhang |
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| Abstract: | To address the problem that traditional aircraft detection methods have low detection accuracy on remote sensing images with complex backgrounds and dense targets， an improved remote sensing image aircraft target detection algorithm based on Faster-RCNN （Faster-Regions with Convolutional Neural Network） is proposed. ResNet50 is used as the basic feature extraction network of the algorithm， and the dilated bottlenecks are introduced for multi-layer feature fusion to construct a new feature extraction network， which improve the feature extraction capability of the algorithm. First， the cross-validation training method is used on the UCAS-AOD data set to verify the stability of the model on different training sets and test sets， and compare the detection performance of different algorithms. Then， comparative experiment is conducted on the NWPU VHR-10 data set to verify the generalization of the model. Experimental results showed that： The average precision of the proposed algorithm is 97.1% on the UCAS-AOD data set and 96.2% on the NWPU VHR-10 data set. The study indicated that the proposed algorithm in this paper can not only improve the detection accuracy of aircraft in remote sensing images， but also have a stronger generalization， which has certain reference significance to the rapid detection of aircraft in remote sensing images. |
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| Keywords: | Deep learning Remote sensing image Object detection Feature fusion Dilated bottleneck |
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