Fast target detection method based on YOLOv2 in remote sensing image

A remote sensing image and target detection technology, which is applied in the field of image processing and pattern recognition, to achieve the effect of improving performance, improving generalization ability, and ensuring detection accuracy

Inactive Publication Date: 2018-12-21
JILIN UNIV
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However, in view of the complexity and variability of practical applications, most detection methods are only partially effective, and it is ne

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  • Fast target detection method based on YOLOv2 in remote sensing image
  • Fast target detection method based on YOLOv2 in remote sensing image
  • Fast target detection method based on YOLOv2 in remote sensing image

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[0061] The specific embodiments of the present invention are described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. A method for rapid target detection in remote sensing images based on YOLOv2 includes the following steps:

[0062] 1. Make remote sensing image data sets such as figure 1 As shown, the preprocessing, target labeling and data expansion of the collected remote sensing images include the following steps:

[0063] 1.1 Preprocessing: Use the dark channel defogging algorithm to defog the foggy image, and use the MSRCR image enhancement algorithm to obtain the remote sensing image data set with improved clarity and contrast.

[0064] 1.2 Manually label the remote sensing image data set obtained in step 1.1, divide the target to be detected into six categories: airplanes, ships, vehicles, squares, playgrounds, and buildings, record the coordinates of the target location, and compare the targe...

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Abstract

A remote sensing image fast target detection method based on YOLOv2 belongs to the technical field of image processing and pattern recognition. The invention realizes the fast detection of important targets in the remote sensing image. Firstly, the remote sensing image dataset is constructed for the training and performance testing of the model. Then a convolution neural network structure suitablefor remote sensing image classification is proposed for feature extraction, and then a target detection network is constructed. Aiming at the problem that the detection capability of the convolutionneural network to the small target is poor, the invention adopts the methods of increasing the training scale and the batch regularization to improve the performance of the network. The invention defines the offset factor to correct the target position, and utilizes the SVM classifier to carry out the secondary classification of the target background on the detection result, which ensures the detection precision and the detection speed, and realizes the end-to-end detection. Most importantly, the model allows the new data detection results to be extended to the training dataset, thus updatingthe training target detection network and improving the generalization ability of the model.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a method for fast target detection of remote sensing images based on YOLOv2. Background technique [0002] With its rapid development and its unique advantages in obtaining ground information, remote sensing technology is widely used in various fields of military and national economy. Using the remote sensing image processing system to accurately search, discover and identify various important targets, and realize the rapid transformation of remote sensing image data into useful information, not only can save human resources, but more importantly, it can improve the efficiency of information acquisition and give full play to the advantages of remote sensing detection . Therefore, how to quickly and accurately mine key target information from massive remote sensing images has become a crucial issue. At present, my country's use of rem...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/50G06V2201/07G06N3/045G06F18/2411G06F18/214
Inventor 王世刚李奇赵岩韦健赵文婷卢洋
Owner JILIN UNIV
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