Mouse motion state analysis method based on deep learning algorithm

Abstract

The invention provides an animal motion state video analysis method based on a deep learning algorithm. The method comprises the following steps: segmenting each frame of a mouse motion video, adjusting the size of a picture, and generating an image of a mouse motion state; carrying out a small amount of manual annotation on the images of the mouse motion state, and making an image data set required by the training model; training a mouse motion state analysis model based on the manufactured data set; and inputting the mouse motion video to be analyzed into the trained mouse motion state analysis model to obtain an analysis result of the mouse motion state. According to the mouse motion state analysis method, through a convolutional neural network technology and a transfer learning technology in deep learning, under the condition that only a small amount of video data is labeled and data distribution is unbalanced, and therefore, an end-to-end mouse motion state analysis model with high training speed, high accuracy, high inference speed and highdeduction speed is constructed.

Description

technical field [0001] The invention relates to the field of animal motion state analysis, in particular to a mouse motion state analysis method based on a deep learning algorithm. Background technique [0002] The brain is often seen as a symbol of intelligence. Researchers in the field of neuroscience have been working hard to understand the structure of the brain and try to decipher its secrets. But at present, the work of basic neuroscience research in the human brain is still very difficult. The human brain has a delicate structure and complex connections, which may not be thoroughly studied by the current scientific level of human beings. Therefore, researchers have turned their attention to small rodents whose brain neurons are a few orders of magnitude compared to humans. [0003] Previously, in many populations of rodents, researchers have discovered the learning and cognitive patterns of the neural networks in their brains, that is, using a highly sparse and abst...

AI Technical Summary

Problems solved by technology

[0008] The current problem is that it is still a very difficult task to efficiently and easily observe the movement state of animals and detect important behaviors

The traditional computer vision method has slow recognition speed and low recognition accuracy; while the end-to-end method using the deep learning model has fast recognition speed and high recognition accuracy, but it often requires manual labeling of a large amount of training data, which makes the model training cost lower. high

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