An Intelligent Floating Object Salvage System Based on Neural Network and Image Recognition

Abstract

The present invention claims protection for an intelligent surface floating object salvage system based on neural network and image recognition, which relates to the field of intelligent recognition and automatic control, including 1) catamaran structure modeling; 2) image acquisition and processing; 3) image recognition model Modeling; 4) Hull control model modeling, according to the comparison between the coordinates of the floating objects identified in the image and the coordinates of the third bisector of the recognition area, the deflection direction at the next moment is obtained through a linear function; 5) Intelligent water surface floating object salvage The construction of the system consists of assembling a single-chip microcomputer, battery, engine, radiator, salvage net, wide-angle camera, PC and other equipment into an intelligent water surface floating object salvage ship, and then burning the constructed hull control program into the intelligent water surface floating object identification system In the single-chip microcomputer, the functions such as steering, cruising, and U-turn of the intelligent water surface floating object identification system are realized. The present invention can automatically, quickly and efficiently salvage the water surface floating objects without human control, and realize autonomous dredging.

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...

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.

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