基于优化Faster-RCNN的遥感影像飞机检测

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，引入空洞残差块进行多层特征融合，构建新的特征提取网络，提高算法的特征提取能力。首先在UCAS-AOD数据集上采用交叉验证训练方法验证模型在不同训练集与测试集上的稳定性，同时比较不同算法的检测性能；然后在NWPU VHR-10数据集上进行飞机检测对比实验，验证模型泛化性。实验结果表明：在UCAS-AOD数据集上优化算法平均精度为97.1%，在NWPU VHR-10数据集上优化算法平均精度为96.2%。该优化算法能够提升遥感影像中飞机的检测精度，且泛化性更强，对实现遥感影像飞机快速检测具有一定的参考意义。

Aircraft Detection in Remote Sensing Image based on Optimized Faster-RCNN

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.