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Remote sensing image target detection based on a multi-scale deep feature fusion network

  • role： First author第一作者
  • Affiliation：

    Hohai University, College of Internet of Things Engineering, Changzhou 213022, China

  • Email：fanxn@hhuc.edu.cn
  • Introduction：范新南，研究方向为图像处理和机器视觉。E-mail： fanxn@hhuc.edu.cn
FAN Xinnan,  
  • Affiliation：

    Hohai University, College of Internet of Things Engineering, Changzhou 213022, China

YAN Wei,  
  • role： Corresponding author通信作者
  • Affiliation：

    Hohai University, College of Internet of Things Engineering, Changzhou 213022, China

  • Email：shipf@hhu.edu.cn
  • Introduction：史朋飞，研究方向为多源信息融合和图像处理。E-mail： shipf@hhu.edu.cn
SHI Pengfei*,  
  • Affiliation：

    Hohai University, College of Internet of Things Engineering, Changzhou 213022, China

ZHANG Xuewu

Resümee

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.

Schlüsselwort

remote sensing image;object detection;feature extraction network;Feature Pyramid Network;loss function;data augmentation

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