Military target identification method based on deep learning
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A deep learning and target recognition technology, applied in the field of automatic target recognition, can solve problems such as the inability to give accurate judgments quickly and timely, the target not being found, and the delay of fighters.
Inactive Publication Date: 2018-10-12
HANGZHOU DIANZI UNIV
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[0003] In the face of massive observation images and video data presented at the TB / PB level, there is a situation of "finding a needle in a haystack" in the application. On the one hand, the data is too large to be processed, and on the other hand, the required target cannot be found, resulting in the inability to quickly and timely Give accurate judgments and delay fighters
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[0062] The military target recognition method based on deep learning provided by the present invention will be further described below in conjunction with the accompanying drawings.
[0063] Aiming at the relevant technical problems existing in the prior art, the present invention starts from the concept of intelligent recognition of military targets and combines the most advanced technical means of deep learning in target detection to propose a target recognition method based on a dense convolutional neural network. It can accurately detect military targets such as aircraft, tanks, warships, missiles, submarines, cannons, helicopters, guns, and soldiers.
[0064] In order to solve the technical problems existing in the prior art, the present invention proposes a military target recognition method based on deep learning——DRFCN, specifically as figure 1 , including the following steps (1): Generate as few high-quality sampling regions as possible through a region sampling algor...
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Abstract
The invention discloses a military target identification method based on deep learning, and belongs to the field of automatic target identification based on images. The military target identificationmethod based on deep learning disclosed by the invention can be used in the fields, such as military target identification, large-scale military target retrieving, weapon equipment intellectualizationand situation estimation. According to the method disclosed by the invention, for the problems that each layer is in one-way connection and the feature expression capability is insufficient in the traditional target identification network based on deep learning, an algorithm model of a dense connected convolutional layer is re-designed; a dense connection mode is used; the algorithm model multiplexes features of each layer; therefore, the average target identification accuracy rate of the algorithm model is increased; the algorithm model obtained by training in this way is relatively small; and simultaneously, the algorithm model solves the problems of gradient diffusion and gradient expansion.
Description
technical field [0001] The invention belongs to the field of image-based automatic target recognition, and in particular relates to a deep learning-based military target recognition method. Background technique [0002] Under the new situation, networked joint operations present the characteristics of land, sea, air, space, electromagnetic, and cyber multi-dimensional battlefield integration operations. Carrier, surface, underwater, ship mount, database system), multi-source sensors (SAR / ISAR, infrared camera, hyperspectral / multispectral / low light / EO / visible light, sonar, laser, millimeter wave) and other ways can obtain Massive image and video data, the data source has the characteristics of "5V+1C", namely: Volume (large capacity), Variety (diversity), Velocity (timeliness) and Veracity (accuracy), Value (value) and Complexity (Complexity). Therefore, how to find out the required military target category and location information from these massive images and video big da...
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