Two-dimensional image identification method for automatic identification of aquaculture net cage

Abstract

The invention discloses a two-dimensional image identification method for automatic identification of an aquaculture net cage, and the method comprises the following steps: S1, dividing a two-dimensional image space into X * Y parts at equal intervals in the X and Y directions under a rectangular coordinate system, forming X * Y color blocks, and determining the color value ranges of the color blocks; S2, dividing all color blocks into three types of functional regions, wherein the first region is used for positioning and correcting a two-dimensional image; encoding the machine identificationinformation in the second area; coding machine identification information and printing human eye identification characters in the third area at the same time; S3, determining the color definition of each color block in the first area; determining a coding rule and a color definition of each color block in the second region; determining a coding rule and color definition of each color block in thethird area; and S4, identifying the two-dimensional image according to the coding rules and color definitions of the color blocks. According to the invention, reliable identification can be carried out under the condition that fouling reaches 60%.

Description

technical field [0001] The invention relates to a two-dimensional image identification method, in particular to a two-dimensional image identification method for automatic identification of breeding cages. Background technique [0002] In order to effectively carry out fish farming management, farming enterprises or farmers have marked the cages that can be recognized by human eyes, including writing numbers with paint. These traditional identification methods are extremely difficult to identify by human eyes in most cases due to biological adhesion, and the success probability of automatic identification by machine vision is extremely low, which is not conducive to automated breeding management. [0003] Existing two-dimensional code codes can also be used for the identification of cages, but limited to the error correction capability of existing two-dimensional codes, the highest level can only reach 30% (L level: 7%; M level: 15%; Q Class H: 25%; Class H: 30%), this meth...

AI Technical Summary

Problems solved by technology

These traditional identification methods are extremely difficult to identify by human eyes in most cases due to biological adhesion, and the success probability of automatic identification by machine vision is extremely low, which is not conducive to automated breeding management

[0003] Existing two-dimensional code codes can also be used for the identification of cages, but limited to the error correction capability of existing two-dimensional codes, the highest level can only reach 30% (L level: 7%; M level: 15%; Q Class H: 25%; Class H: 30%), this method cannot meet the requirements for automatic identification of mariculture (fouling caused by biofouling exceeds 50%)

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