Convolutional neural network (CNN) based egg embryo classification method

Abstract

The invention provides a CNN based egg embryo classification method. The method comprises that egg embryo images in nine days are collected and divided into two types of samples according to the survival state of an embryo; the embryo images are preprocessed, ROI are extracted from the images, and the sizes of the images are normalized; a channel weighting and combined monitoring combined CNN network structure (SJ-CNN) is used to train a target set; and a trained model is used to classify the images to be detected. Compared with a traditional scheme, a complex image processing process is avoided and is not influenced by incomplete image vessel segmentation or violent noise, the method is highly adaptive to a new sample, and high-precision classification can be realized.

Description

technical field [0001] The invention relates to image processing, channel weighting, joint supervision and convolutional neural network, in particular to an embryo classification method. Background technique [0002] At present, in engineering, most of the viability detection and classification of chicken embryos adopt traditional artificial egg detection, and the viability of egg embryos is detected by judging the blood vessels of egg embryos with human eyes, but the method of human eye examination is easily interfered by subjective factors, and There are disadvantages such as visual fatigue and low detection efficiency, which are prone to false detection and missed detection, and it is difficult to meet the high standard requirements of the modern embryo detection and classification industry. Therefore, egg embryo classification detection technology has been extensively studied. [0003] There are many studies on the viability detection and classification of egg embryos us...

AI Technical Summary

Problems solved by technology

[0002] At present, in engineering, most of the viability detection and classification of chicken embryos adopt traditional artificial egg detection, and the viability of egg embryos is detected by judging the blood vessels of egg embryos with human eyes, but the method of human eye examination is easily interfered by subjective factors, and There are disadvantages such as visual fatigue and low detection efficiency, which are prone to false detection and missed detection, and it is difficult to meet the high standard requirements of the modern embryo detection and classification industry. Therefore, egg embryo classification detection technology has been extensively studied

[0003] There are many studies on the viability detection and classification of egg embryos using machine vision technology. Machine vision technology replaces traditional manual inspection, and uses specific algorithms for image processing to carry out fine digital analysis and processing of embryo images, avoiding traditional manual inspection. However, the image processing process of this method is too cumbersome and the accuracy rate is not high

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