Double-attention remote sensing small target detection method based on FPN and PAN networks

A technology of small target detection and attention, which is applied in the field of image application and computer vision, can solve the problem that the detail information of small target objects is easy to be lost, and achieve the effect of improving detailed texture information, enhancing positioning performance, and alleviating interference

Pending Publication Date: 2022-07-29
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

Although these multi-scale detection methods have different degrees of improvement in small target detection performance through different feature fusion methods, they have not been tested in remote sensing images, ignoring the impact of complex background information in remote sensing images. The problem of adverse effects caused by small objects and the problem that the details of small objects in remote sensing images are more likely to be lost

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  • Double-attention remote sensing small target detection method based on FPN and PAN networks
  • Double-attention remote sensing small target detection method based on FPN and PAN networks

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Embodiment 1

[0051] Example 1: as figure 1 It is a schematic flowchart of the method of the present invention, a kind of remote sensing small target detection method based on the dual attention of FPN and PAN network, comprising:

[0052] Step1 Extract the feature map pyramid of the remote sensing image: extract the remote sensing image features through the feature extraction network to generate the feature map pyramid;

[0053] Using the remote sensing images in the DIOR remote sensing image dataset as the input image, the CSPDark-53 feature extraction network in YOLOv5 is used to extract features from the input remote sensing images containing small targets of ships, and select three different stages in the feature extraction network that contain ships. The feature maps of the target features form a feature map pyramid, in which each layer of feature maps has different sizes and different channels, expressed as C={C 2 ,C 3 ,C 4 }. It is used for cross-layer fusion in the subsequent F...

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Abstract

The invention relates to a dual-attention remote sensing small target detection method based on FPN and PAN networks, and belongs to the field of computer vision. The method comprises the following steps of: pooling a top-layer feature map in the FPN network to obtain a channel vector, performing matrix operation on the channel vector to obtain a channel attention matrix, normalizing the channel attention matrix to obtain a channel weight matrix, multiplying the weight into the feature map to obtain a feature map with channel weight, and fusing the feature map with low-layer features. In a PAN network, channel compression is firstly carried out on a bottom-layer feature map to obtain a spatial vector, then matrix operation is carried out on the spatial vector to obtain a spatial attention matrix, then the spatial attention matrix is normalized to obtain a spatial weight matrix, the weight is multiplied into the feature map to obtain a feature map with spatial weight, and the feature map with the spatial weight is fused with high-layer features. And finally sending to a detection head to generate a detection result. According to the method, the target detection precision can be enhanced in remote sensing image detection, and interference caused by complex background information in the remote sensing image is overcome.

Description

technical field [0001] The invention relates to a remote sensing small target detection method based on double attention of FPN and PAN network, and belongs to the fields of computer vision and image application. Background technique [0002] Remote sensing small target detection has broad prospects in various fields such as vehicle control and ship scheduling. There are many ways to detect and locate small objects from images captured by satellites or drones. However, the detection performance is not ideal for noisy and low-resolution remote sensing images. Existing detection methods based on deep learning can be roughly divided into two categories: one is a two-stage target detection method represented by Faster R-CNN, which first inputs the feature map output from the backbone into the RPN network (Region Proposal). Network), the input samples are mapped into a probability value and four coordinate values. Accurate candidate regions are obtained through training, and t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06V10/426G06V10/80G06V10/82G06K9/62G06N3/04
CPCG06N3/045G06F18/253
Inventor 李凡韩兴勃
Owner KUNMING UNIV OF SCI & TECH
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