Weak supervision remote sensing target detection method based on hybrid hole convolution

A target detection, weakly supervised technology, applied in the field of weakly supervised remote sensing target detection based on hybrid hole convolution, can solve the problems of large manpower and material resources, lack of labeled data sets, unfavorable training, etc., to avoid loss and improve robustness sexual effect

Pending Publication Date: 2021-01-05
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

However, this type of method generally predicts multi-scale feature maps separately, and the network will become very complicated and not conducive to training
[0005] In addition, another important problem in the field of remote sensing image target detection is the lack of labeled data sets.

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  • Weak supervision remote sensing target detection method based on hybrid hole convolution
  • Weak supervision remote sensing target detection method based on hybrid hole convolution
  • Weak supervision remote sensing target detection method based on hybrid hole convolution

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Embodiment Construction

[0059] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0060] A weakly supervised remote sensing image multi-target detection method based on collaborative learning according to the present invention, the algorithm framework is shown in Figure 1, including the following steps:

[0061] (1) Obtain the remote sensing image data set to be detected, and divide the data set into training set, verification set and test set in proportion;

[0062] The remote sensing image data used in this embodiment are TGRS-HRRSD and DIOR data sets. Among them, TGRS-HRRSD contains a total of 21,761 high-altitude images from Google Earth and Baidu Maps, including 13 categories of 55,740 target object instances; target instance.

[0063] In this embodiment, the Pytorch framework is adopted, and the programming experiment is carried out in combination with the python language. Pytorch can be regarded as...

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Abstract

The invention provides a weak supervision remote sensing target detection method based on hybrid hole convolution. According to the method, various customized designs such as hybrid hole convolution,attention mechanism and multi-layer pooling are adopted, multi-scale feature extraction and fusion are enhanced, and the robustness of objects of different sizes is improved. Besides, asynchronous iteration alternate training between a strong supervision detector and a weak supervision detector is utilized, training and detection can be carried out only through an image-level real label, and the purpose of cooperatively improving the detection performance is achieved.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to a weakly supervised remote sensing object detection method based on mixed hole convolution. Background technique [0002] With the development and combination of aviation technology and computer vision technology, high-altitude high-resolution optical remote sensing images are becoming easier to obtain and are used in various fields. As a basic feature extraction problem in remote sensing image analysis, the research on this field has a long history in academia. Specifically, the targets of remote sensing image target detection include the location of ground objects and the classification of object categories. In recent years, research results in the field of remote sensing image target detection have progressed rapidly, and many algorithms can simultaneously achieve high-precision ground object positioning and recognition. Among them, most of them decompose the image feature...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/13G06V2201/07G06N3/045G06F18/253G06F18/214
Inventor 陈苏婷邵东威张闯
Owner NANJING UNIV OF INFORMATION SCI & TECH
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