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A cascaded coarse-to-fine convolutional neural network method for ship type identification

A neural network and type recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of time-consuming acquisition of ship type information, adverse maritime supervision efficiency, etc., to ensure maritime traffic safety, realize automation and safety. High-precision identification and the effect of improving navigation efficiency

Inactive Publication Date: 2019-02-01
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

Obtaining ship type information by traditional technical means is very time-consuming, which is not conducive to improving the efficiency of maritime supervision

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  • A cascaded coarse-to-fine convolutional neural network method for ship type identification
  • A cascaded coarse-to-fine convolutional neural network method for ship type identification
  • A cascaded coarse-to-fine convolutional neural network method for ship type identification

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[0055] In order to better understand the technical features, objectives and effects of the present invention, the present invention will be described in more detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention and not used to limit the patent of the present invention. It should be noted that these drawings all adopt a very simplified form and all use imprecise ratios, which are only used to facilitate and clearly assist in explaining the patent of the present invention.

[0056] This embodiment provides a cascaded deep convolutional neural network ship type identification method from coarse to fine, refer to the attachment figure 1 As shown in the schematic diagram of the overall process, the method includes the following steps:

[0057] S1: Input the pictures of all ship types and the corresponding picture labels, and perform coarse-level training on the deep ...

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Abstract

The invention provides a ship type identification method of cascade type deep convolution neural network from coarse to fine, This method adopts random heuristic selection mechanism to dynamically adjust the structure and parameter settings of the depth network. The depth convolution neural network which can identify the ship type is trained by two steps of rough training and fine training. The rough training process is similar to the training process of the traditional depth convolution neural network, and the input sample of the training process is the ship picture. In order to improve the overall accuracy of ship type recognition, the depth convolution neural network is trained again in the fine-level training process for the merchant ship images with the lowest ship type recognition accuracy in the rough-level training process. The method of the invention can obtain better identification accuracy for different ship types, and provides information support for automatic ship type identification and intelligent navigation of ships.

Description

Technical field [0001] The invention relates to the technical field of maritime video monitoring, in particular to a cascaded coarse-to-fine convolutional neural network ship type identification method. Background technique [0002] Currently, Vessel Traffic Service (VTS) and Automatic Identification System (AIS) are the main means to obtain ship type information. After the ship enters the VTS reporting line, the crew on board will report the ship’s basic information, such as port of destination, port of departure, and ship type, to the maritime regulatory authority via VHF telephone. In addition, the AIS system will periodically distribute its own ship’s static and dynamic information through broadcasting, including its ship’s type, position, call sign, ship name, gross tonnage, ship’s draft and speed, etc. However, AIS users need to manually enter static information such as ship type and ship call sign for the AIS system in advance. From the above analysis, it can be seen tha...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/41G06F18/2413
Inventor 陈信强杨勇生吴华锋苌道方于泽崴张倩楠陈晶傅俊杰赵建森陈辉兴
Owner SHANGHAI MARITIME UNIVERSITY
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