Fish body posture and length automatic analysis method based on key point detection and deep convolutional neural network

Abstract

The invention relates to a fish body posture and length automatic analysis method based on key point detection and a deep convolutional neural network, and the method comprises the steps: S1, obtaining binocular images comprising a fish school through an underwater binocular camera, wherein the binocular images comprise a left image and a right image; and S2, performing calibrating in an underwater environment to obtain binocular camera parameters, and performing binocular correction on the obtained binocular image. The beneficial effects of the invention are that the method combines the deepconvolution neural network, and is high in adaptability to an application environment and a scene; a key point detection idea is introduced, and only the spatial positions of specific key points on fish bodies are concerned, so that the difficulty of global binocular matching in underwater application is avoided; required equipment is simple, and only an underwater binocular camera and an operation rear end are required; attitude estimation and length measurement can be carried out on multiple fishes with different positions and attitudes in the image in real time, and the accuracy and the efficiency are relatively high; and the model also has generalization ability for tasks, and is easy to migrate from one working scene to another.

Description

technical field [0001] The invention relates to the fields of aquaculture and underwater biological measurement, and particularly includes an automatic analysis method for fish body posture and length based on key point detection and deep convolutional neural network. Background technique [0002] In the fish farming industry, practitioners need to assess the growth status of the fish being farmed to determine further farming strategies. The length information of the fish body can most intuitively reflect the growth of the fish school, which is of great significance to the monitoring and evaluation of the growth status of the fish school. In order to improve the scientificity and efficiency of fish farming, it is very important to obtain accurate fish body length information conveniently and efficiently in fish farming. [0003] Traditional fish length monitoring methods rely on relatively heavy human resources and lack efficiency. Farmers need to catch a part of the fish,...

AI Technical Summary

Problems solved by technology

In terms of length estimation, most methods based on machine vision apply global or semi-global matching to estimate the disparity of each pixel of the binocular image, thereby projecting the body weight of the fish into the world coordinate system. The underwater environment often has the influence of floating objects, turbidity and light. These pixel-level matching algorithms often cannot obtain high accuracy, which will directly lead to large errors in the estimation of fish body length.

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