Fish fine-grained classification method based on deep learning

Abstract

The invention discloses a fish fine-grained classification method based on deep learning. The method comprises the steps of preprocessing an acquired image; carrying out feature extraction by using adeep neural network; constructing a feature pyramid network for regional proposal; carrying out cutting and feature extraction on a proposed area, then carrying out primary classification by utilizingextracted features on one hand, inputting the classification accuracy into an area proposal network as a supervision signal on the other hand, fusing the features and full-graph features, sending thefused features into a full-connection layer for classification, and outputting a final classification result. According to the invention, when an existing object classification technology carries outa fine-grained classification task, a fine-grained classification task is carried out; due to the problems of complex environment, fine inter-class difference between classes and low accuracy causedby large intra-class difference, when the method is used for performing fine-grained classification on the fish images under the complex background, the identification time is short, the identification accuracy is high, extra label information is not needed, and the method is suitable for popularization and application.

Description

technical field [0001] The invention relates to the technical fields of image processing, its vision, and neural network, and in particular to a method for fine-grained classification of fish based on deep learning. Background technique [0002] Fine-grained image classification (Fine-Grained Categorization), also known as sub-category image classification (Sub-Category Recognition), is a very popular research topic in the fields of computer vision and pattern recognition in recent years. Its purpose is to divide the coarse-grained large categories into more detailed subcategories. However, due to the subtle inter-class differences and large intra-class differences between subcategories, fine-grained image classification is more difficult than ordinary image classification tasks. . Fine-grained classification of fish has great commercial application in fisheries. Compared with the traditional manual identification method, the fish fine-grained classification method based o...

AI Technical Summary

Problems solved by technology

Priori-based classification algorithms often cannot effectively solve the difficulties caused by large intra-class gaps and small inter-class gaps in fish fine-grained classification tasks, resulting in low classification accuracy and unable to meet application requirements

The fine-grained classification algorithm based on learning can achieve better classification accuracy, but at the same time it also faces the problems of long recognition time, low recognition accuracy, and the need for additional label information, which increases time and labor costs and reduces fishing industry. work efficiency and effectiveness

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