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Feature attention pyramid-based remote sensing image object detection method

  • role： First author第一作者
  • Affiliation：

    School of Computer Science, Shaanxi Normal University, Xi'an 710119, China

  • Email：wangxili@snnu.edu.cn
  • Introduction：汪西莉，研究方向为智能信号处理、模式识别、图像处理。E-mail： wangxili@snnu.edu.cn
WANG Xili,  
  • Affiliation：

    School of Computer Science, Shaanxi Normal University, Xi'an 710119, China

LIANG Zhengyin,  
  • Affiliation：

    School of Computer Science, Shaanxi Normal University, Xi'an 710119, China

LIU Tao

Resümee

The characteristics of remote sensing images, such as complex scenes, different sizes of targets and unbalanced distributions, increase the difficulty of target detection. However, feature pyramids that are suitable for detecting targets of different scales do not consider the importance of different feature maps when fusing the feature maps, let alone emphasize the features of target areas. For this purpose, this paper proposes a feature attention pyramid-based remote sensing image object detection method (namely, the feature attention pyramid network, FAPNet).First, the feature maps of different depths are fused by channel concatenation, and the features of different sized receptive fields are provided for the feature maps used for detection. The channel attention mechanism is used to recalibrate the fused feature maps in the channel dimension. The feature maps from different depths are adaptively adjusted according to the scale of the object to be detected to strengthen the feature that matches highly between the size of the receptive field and the object to be detected and weaken the feature with a low degree of matching. Second, the weakly supervised attention module uses the superimposed atrous spatial pyramid pooling structure and convolutional segmentation module to model spatial attention weights to adjust the feature distribution of the feature map used for prediction, strengthen the object area feature, and weaken the background area feature, which further improves the performance of object detection methods.The experimental results show that compared with RetinaNet, the proposed method improves the accuracy (AP) for car targets by 3.41% and 2.26% on the UCAS-AOD dataset and RSOD dataset, respectively, achieves better AP results on each target for multiclass targets and is superior to other comparative object detection methods on the mAP indicator for multitargets.A feature attention pyramid-based remote sensing image object detection method is proposed in this paper. Its contribution lies in the designed feature attention pyramid module and weakly supervised attention module. With the new modules, the proposed method can extract target features more accurately in complex scenes with targets of different sizes by channel attention and spatial attention, thus improving the performance of detection. The experimental results show that the proposed method is superior to the RetinaNet and FAN methods and is more suitable for remote sensing image object detection tasks with complex scenes and multiscale targets.

Schlüsselwort

remote sensing image;object detection;weakly supervised segmentation;attention mechanism;feature pyramid

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