Satellite-borne target fusion detection method based on rotating region convolutional neural network

A technology of convolutional neural network and detection method, which is applied in the field of fusion detection of spaceborne targets based on convolutional neural networks of rotating regions, which can solve the problems of difficulty, density, and high resolution of satellite images in target detection.

Pending Publication Date: 2020-11-06
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

Therefore, there are four main issues that the algorithm needs to consider: 1. Satellite images usually have high resolution, which brings difficulty to target detection; 2. Different from commonly used data sets such as PASCAL-VOC and ImageNet, in satellite image data sets Objects such as ships, small vehicles, and airplanes may be very small (less than 15×15 pixels) and dense; 3. The relative lack of public data sets; 4. The problem of complete rotation invariance, many target objects, such as cars, boats and airplanes, etc., There are many directions when looking down

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  • Satellite-borne target fusion detection method based on rotating region convolutional neural network

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[0081] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings, so as to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0082] In the drawings, components with the same structure are denoted by the same numerals, and components with similar structures or functions are denoted by similar numerals. The size and thickness of each component shown in the drawings are shown arbitrarily, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of parts is appropriately exaggerated in some places in the drawings.

[0083] Such as figure 1As shown, a spaceborne target fusion detection method based on rotating area convolutional neural network i...

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Abstract

The invention discloses a satellite-borne target fusion detection method based on a rotating region convolutional neural network, and relates to the field of satellite-borne target detection. The invention is oriented to intelligent computing platforms such as a universal parallel computing architecture processor, a field programmable gate array, a system-on-chip (SOC) and the like. The method comprises the following steps: firstly, fusing a panchromatic (PAN) image and a multispectral (MS) image by adopting a panchromatic sharpening method, and then detecting a target in the fused spaceborneimage by adopting a target detection framework based on a rotating region convolutional neural network, wherein the target detection framework is based on a Faster R-CNN structure. According to the invention, the detection accuracy of the small target in the multi-source satellite-borne image is improved, the target detection framework has good generalization ability, and the method can be widelyapplied to the fields of target detection, safety monitoring and the like.

Description

technical field [0001] The invention relates to the field of spaceborne target detection, in particular to a spaceborne target fusion detection method based on a rotating area convolutional neural network. Background technique [0002] Object detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial inspection, aerospace and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Therefore, target detection has become a research hotspot in theory and application in recent years. It is an important branch of image processing and computer vision, and it is also the core part of an intelligent monitoring system. At the same time, target detection is also a basic algorithm for target recognition. It plays a vital role. Since AlexNet, the Convolutional Neural Networks (CNN) model built by Hinton's researc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04
CPCG06V20/194G06V20/13G06V10/25G06N3/045G06F18/2135G06F18/251
Inventor 敬忠良押莹潘汉李旻哲
Owner SHANGHAI JIAO TONG UNIV
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