Juvenile fish limb identification method based on multi-scale cascaded perceptual convolutional neural network

Abstract

The present disclosure provides a juvenile fish limb identification method based on a multi-scale cascaded perceptual convolutional neural network. The method includes the following steps: acquiring a video sequence of a juvenile fish, dividing a fish body into five non-overlapping parts, performing semantic annotation on the five non-overlapping parts, and taking the five non-overlapping parts as an input of the multi-scale cascaded perceptual convolutional neural network; and using a convolutional layer as a feature extractor, performing feature extraction on an input image containing the annotation of each limb, inputting extracted features into an Attention-region proposal network (RPN) structure, determining a category of each pixel, and generating a limb mask of each limb category using a multi-scale cascade method. According to the method, the limbs of the juvenile fish can be efficiently and accurately identified, and technical support is provided for posture quantification of the juvenile fish.