YOLO v5-based attached marine organism type identification method

Abstract

The invention discloses a YOLO v5-based attached marine organism type identification method, which specifically comprises the steps of collecting marine organism attachment pictures to construct a data set, and dividing the constructed data set into a training set, a test set and a verification set; adding 1*1 convolution kernels in front of the first 3*3 convolution layer and the second 3*3 convolution layer of the Backbone of the YOLO v5 algorithm; adding a Non-local attention mechanism module respectively in front of the third BottleneckCSP of the Backbone and in front of the fourth BottleneckCSP of the Backbone; using a CSPNet structure composed of a Bottleneck and a standard convolution layer in the Backbone, and adding a detection layer of Output; introducing a PANet structure in the Neck; and generating an improved YOLO v5 algorithm, and finally, performing training by utilizing a data set, so that the accuracy of identifying the types of the attached marine organisms is higher, a good detection effect on small targets is achieved, the missing detection risk is reduced, and the detection performance of the traditional YOLO v5 algorithm is improved.

Description

technical field [0001] The invention relates to the technical field of image target detection, in particular to a YOLO v5-based method for identifying species of attached marine organisms. Background technique [0002] The ocean occupies 71% of the earth's area, providing human beings with abundant resources and potential wealth. In terms of global GDP, the income from marine resources development is as high as 7 trillion US dollars per year, and 90% of international trade transportation is undertaken by ocean transportation. [0003] With the continuous development of the ocean by humans, some problems have also been exposed. In coastal power plants, ships, and buildings in seawater, most of the substrate surfaces will be adhered by marine organisms, which will increase flow resistance and form localized corrosion cells, which will accelerate the corrosion rate of the substrate and cause unnecessary property losses. For example, most coastal power plants use seawater as t...

AI Technical Summary

Problems solved by technology

Among them, the YOLO v5 algorithm of the convolutional neural network is used to identify and detect the species of attached marine organisms, but the current detection algorithm has the problems of low recognition accuracy and missed detection.

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