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Intelligent water surface floating object salvage system based on neural network and image recognition

A technology for floating objects on the water surface and image recognition, which is applied in the field of neural network and image recognition, can solve the problem of reducing feature resolution, etc., and achieve the effects of simple and accurate control, accurate image recognition, and convenient writing

Active Publication Date: 2019-03-15
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

Each convolutional layer in the convolutional neural network is followed by a calculation layer for local averaging and secondary extraction. This unique feature extraction structure reduces the feature resolution.

Method used

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  • Intelligent water surface floating object salvage system based on neural network and image recognition
  • Intelligent water surface floating object salvage system based on neural network and image recognition
  • Intelligent water surface floating object salvage system based on neural network and image recognition

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Embodiment Construction

[0038] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0039] The technical scheme that the present invention solves the problems of the technologies described above is:

[0040] refer to figure 1 , figure 1 A flow chart of salvaging floating objects on the water surface based on image processing and image recognition is provided for the embodiment of the present invention, specifically including:

[0041] S101: Catamaran structure model: Under the condition of carrying an image processing and analysis platform, the stability of the hull is an important factor that cannot be ignored. Therefore, this system adopts the structure of a catamaran, and the hull structure (reference figure 2 ) is divided into three parts: 1. Two monohulls (called the hull) 2. Co...

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Abstract

The invention provides an intelligent water surface floating object salvage system based on a neural network and image recognition, and relates to the fields of intelligent identification and automatic control. The system comprises 1) catamaran structure modeling; 2) image acquisition and processing; 3) image recognition model modeling; 4) hull control model modeling, namely determining the deflection direction at the next moment according to the comparison of coordinates of a floating object recognized by images and three bisector coordinates of a recognition area by a linear function; and 5)building of the intelligent surface floating object salvage system, namely assembling a single-chip microcomputer, a battery, an engine, a radiator, a salvage network, a wide-angle camera, a PC and other components to form an intelligent surface floating object salvage ship, and then burning a built hull control program into the single-chip microcomputer of an intelligent surface floating objectrecognition system to achieve the functions of steering, cruising, turning round and the like of the intelligent surface floating object recognition system. By adopting the system, floating objects onthe water surface can be automatically, quickly and efficiently salvaged without human control, and the independent dredging can be realized.

Description

technical field [0001] The invention belongs to neural networks and related methods of image recognition, and specifically relates to the fields of convolutional neural networks, image processing and automatic control. Background technique [0002] Convolutional Neural Network (CNN) is a feedforward neural network. Its artificial neurons can respond to surrounding units within a part of the coverage area, and it has excellent performance for large-scale image processing. It includes convolutional layers and pooling layers. [0003] Convolutional neural network is an efficient recognition method that has been developed in recent years and has attracted widespread attention. In the 1960s, Hubel and Wiesel found that its unique network structure can effectively reduce the complexity of the feedback neural network when studying the neurons used for local sensitivity and direction selection in the cat cerebral cortex, and then proposed the convolutional neural network ( Convolu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B63C7/00G06K9/62
CPCB63C7/00G06F18/2413
Inventor 邓欣刘瑞米建勋明伟胡家宾王泽鸿王进孙开伟
Owner CHONGQING UNIV OF POSTS & TELECOMM
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