Convolutional neural network-based unmanned air vehicle to-ground specific target recognition method

A convolutional neural network and target-specific technology, which is applied in the cross-field of aviation and computer vision information processing, can solve problems such as insufficient recognition accuracy and difficult training data sets, so as to improve recognition accuracy, improve training efficiency, and ensure Effect of Recognition Accuracy Requirements

Active Publication Date: 2018-05-08
BEIHANG UNIV
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AI Technical Summary

Problems solved by technology

[0006] The technology of the present invention solves the problem: Overcoming the problem of insufficient recognition accuracy of the traditional method for ground target recognition of UAVs and the difficulty in satisfying the training data set of the deep learning method, it provides a ground-specific UAV

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  • Convolutional neural network-based unmanned air vehicle to-ground specific target recognition method

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0036] 1. Training algorithm

[0037] For the supervised recognition task of convolutional neural network, since the categories of all image samples are known in advance, it is necessary to distribute samples of different categories in different spatial regions according to the distribution of the same image samples in space. After a long time of training the image data set, the parameters of the convolutional neural network are continuously updated, and the boundary positions used to divide the sample space classification are obtained to classify the image. Convolutional neural network is essentially an input-to-output mapping, which learns a function mapping according to specific principles, which maps an input image to a k-dimensional feature vector. As long as the convolutional network is trained and the connection weights between the networks ar...

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Abstract

The invention relates to a convolutional neural network-based unmanned air vehicle to-ground specific target recognition method. The method comprises the following steps of: (1) labeling to-ground specific target data sets acquired by an unmanned air vehicle according to categories, and dividing the to-ground specific target data sets into training sets, verification sets and test sets according to proportions; (2) setting convolutional neural network model training parameters, starting training and obtaining an optimal solution of a convolutional neural network model according to the trainingcondition; (3) changing a depth of the convolutional neural network model, restarting the training in the step (2) to obtain an optimal convolutional neural network model; (4) testing the test set soas to obtain recognition correctness; and (5) applying convolutional neural network model parameters, correctness of which satisfies requirements, to a practical scene of an unmanned air vehicle to-ground specific target, so as to recognize an image target acquired by the unmanned aerial vehicle.

Description

technical field [0001] The invention relates to a method for recognizing a specific target by an unmanned aerial vehicle based on a convolutional neural network, and belongs to the cross field of aviation and computer vision information processing. Background technique [0002] UAV (Umanned Air Vehicle, UAV) is an unmanned aircraft operated by radio remote control equipment and self-contained program control device, or operated completely or intermittently by the on-board computer. UAVs can be divided into military and civilian use according to the application field. In terms of military use, UAVs are divided into reconnaissance aircraft and target aircraft. In terms of civilian use, drones + industry applications are the real rigid needs of drones; currently, they are used in aerial photography, agriculture, plant protection, micro selfies, express delivery, disaster relief, wildlife observation, infectious disease monitoring, surveying and mapping, news reports, power pat...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V20/13G06N3/045
Inventor 张弘罗昭慧张泽宇
Owner BEIHANG UNIV
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