Ship detection method based on deformable fast convolution neural network

A neural network and fast convolution technology, applied in the field of image processing, to achieve the effect of strengthening the detection effect, speeding up the speed, and enhancing the ability of extraction

Active Publication Date: 2019-02-01
XIAMEN UNIV
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AI Technical Summary

Problems solved by technology

[0006] Although the above methods can effectively detect most of the ships in the video or image, there is still a relatively large room for improvement in the detection of speed, accuracy and small-sized targets

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  • Ship detection method based on deformable fast convolution neural network
  • Ship detection method based on deformable fast convolution neural network
  • Ship detection method based on deformable fast convolution neural network

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Embodiment Construction

[0077] The implementation examples and effects of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0078] refer to figure 1 , the implementation steps of the present invention are as follows:

[0079] Step 1, get training set images.

[0080] Existing ship detection method training set images from China Computer Federation (CCF) [6] The game data provided, the data set contains various scenes such as different lighting, different shooting angles, and different weather. Most of the ships are small targets with complex backgrounds, including ports, islands, and sea interference ships. The specific process is as follows:

[0081] In the data set collection stage, the collected data sets are screened to obtain training set images, and each image in the above training set is scaled to a size of 1024×1024 to form the final training data set;

[0082] Step 2, modify the settings in the original Faster R-CNN network, and desig...

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Abstract

The invention relates to a ship detection method based on a deformable fast convolution neural network, which relates to image processing. Detection methods include model training phase and ship detection phase. It can be used in the civil field instead of detecting ships in specific ports, harbors and sea areas in real time, and can be used in video surveillance or ship detection in images, and can also be used in ship detection and monitoring of military ports, so as to discover military intelligence as early as possible, provide operational basis for our military command, and grasp the initiative of the battlefield. By improving the end-to-end method Faster R-CNN considering speed and accuracy, according to the special property of ship target, is the key to the improvement of Faster R-CNN Foundation Network, RoI-wise subnetwork and loss function are modified to obtain the model structure of the deformable fast convolution neural network. The experimental results show that the proposed network is more efficient than the original Faster R-CNN method has better detection speed and accuracy.

Description

technical field [0001] The invention relates to image processing, in particular to a ship detection method based on a deformable fast convolutional neural network. Background technique [0002] Sea ship target detection is a special scene of general target detection. It is the process of finding and locating targets from complex coastal and ocean backgrounds. It has a very wide application prospect and use value. [0003] In recent years, many scholars at home and abroad have done a lot of work on the research of ship target detection algorithm for optical images. It can be roughly divided into detection algorithms based on classification learning, feature discrimination, and Hough voting. However, this type of method is usually sensitive to illumination changes, shooting angles, edge noise, etc., which is easy to cause false alarms, and the algorithm is not robust. [0004] Deep learning is one of the current mainstream machine learning methods, and has achieved great suc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/00G06V2201/07G06F18/214G06F18/24
Inventor 曲延云张怡晨丁瑶陈蓉李翠华
Owner XIAMEN UNIV
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