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范新南，严炜，史朋飞，张学武.2022.多尺度深度特征融合网络的遥感图像目标检测.遥感学报，26（11）： 2292-2303
Fan X N，Yan W，Shi P F and Zhang X W. 2022. Remote sensing image target detection based on a multi-scale deep feature fusion network. National Remote Sensing Bulletin， 26（11）：2292-2303
本文针对现有方法对遥感图像目标检测准确率低的问题，在更快速区域卷积神经网络Faster R-CNN（Faster Region Convolutional Neural Networks）算法的基础上对其进行改进，提出一种新的遥感图像目标检测算法。该算法把Faster R-CNN算法中的VGG（Visual Geometry Group）特征提取网络替换为残差网络ResNet（Residual Networks），在此基础上加入特征金字塔网络以充分表达语义信息和位置信息，并使用焦点损失函数替代Faster R-CNN算法中的交叉熵损失函数以解决难易样本对总损失贡献的权重问题，最后对NWPU VHR-10数据集和RSOD数据集采用数据增广方法以解决数据集中图像样本数量少的问题。为验证本文算法的效果，进行了两组对比实验。第一组实验为本文提出的改进模块在NWPU VHR-10数据集和RSOD数据集上的消融实验；第二组实验为本文算法与其他算法在NWPU VHR-10数据集上的对比实验。实验结果表明，本文算法在NWPU VHR-10数据集和RSOD数据集上的多类平均准确率分别达到93.4%和93.0%，比Faster R-CNN算法提高了10.6%和7.8%。同时也高于现有的其他几种算法。
Some existing target detection algorithms are insufficient for feature extraction in remote sensing images. They cannot solve the difficult problem of large target scale differences in remote sensing images
especially in detecting small targets
resulting in low average detection accuracy. In response to these problems
this paper uses the Faster Region Convolutional Neural Network algorithm as the basic algorithm. Furthermore
it combines the target characteristics in the remote sensing images to improve the basic algorithm. Finally
this paper proposes a new remote sensing image target detection algorithm. First
we use the Residual Network with more powerful feature extraction capabilities to replace the Visual Geometry Group network in the original algorithm. It can solve the shortcomings of the original algorithm’s insufficient feature extraction of the remote sensing images. The deep residual network adopts the identity mapping method
which not only ensures that the performance of the network will not degrade as the network deepens but also extracts deeper features. Second
we add a feature pyramid network to the algorithm to fully integrate feature maps of different scales. The feature map obtained in this way has high-level semantic and low-level detail information. Accordingly
it can take category and location information into account. This approach can greatly solve the difficult problem of large target scale differences in remote sensing images and improve the detection accuracy of small targets to a certain extent. In addition
we use the focal loss function to replace the cross entropy loss function in the original algorithm to solve the problem of the weight of the hard and easy samples to the total loss. Finally
given the problem that the used data set contains a small number of images
we use data augmentation to expand the dataset. This paper carries out two sets of comparative experiments to verify the effect of this algorithm. The first set of experiments is the ablation experiments on the NWPU VHR-10 dataset and RSOD-Dataset of the improved modules proposed in this paper. The second set of experiments is the comparison experiments of the algorithm in this paper and the other comparison algorithms on the NWPU VHR-10 dataset. The results of the first set of ablation experiments show that the various improved modules proposed in this paper can help improve the accuracy of target detection in remote sensing images. For the NWPU VHR-10 dataset
after adding the feature pyramid network
focal loss function
and data augmentation strategy
the algorithm in this paper improves mean Average Precision by 2.6%
4.8%
and 0.8%
respectively. Furthermore
on the RSOD dataset
the algorithm in this paper improves the mean Average Precision by 0.6%
1.6%
and 0.9%
respectively. Accordingly
the target detection accuracy rates of the algorithm in this paper can reach 93.4% and 93.0% on the NWPU VHR-10 dataset and RSOD-Dataset
respectively. The results of the second set of comparative experiments show that the target detection accuracy of the proposed algorithm is better than the comparison algorithm
further proving that the proposed algorithm has good performance in remote sensing image target detection. Finally
compared with BOW
COPD
RICNN
original Faster R-CNN
ODDP
and Mask R-CNN
the algorithm in this paper improves the mean Average Precision by 68.8%
12.7%
20.8%
10.6%
6.7%
and 9.5%
respectively. The remote sensing image target detection algorithm proposed in this paper can better solve the difficult problem of large differences in target scale in remote sensing images. It can improve the target detection accuracy of remote sensing images
especially the detection accuracy of small targets.
遥感图像目标检测特征提取网络特征金字塔网络损失函数数据增广
remote sensing imageobject detectionfeature extraction networkfeature pyramid networkloss functiondata augmentation
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