Deep learning and recognition method for dense bird flock

Abstract

The invention discloses a deep learning and recognition method for a dense bird flock. The method comprises a probability density map generation process and a training process of a full convolutionalneural network. The probability density map generation process comprises the following steps: inputting bird flock photos into a bird flock photo set; making a color table; dotting and marking all birds in the bird flock photo; converting the bird flock photo into a continuous density function by using Gaussian convolution; mapping the continuous density function with a color table lookup table toobtain a corresponding probability density map A. The training process of the full convolutional neural network comprises the following steps: carrying out image addition processing on a bird flock photo; obtaining a bird flock image; establishing an FCNN full convolutional neural network; obtaining a corresponding loss function; inputting the bird flock image into a neural network to obtain a probability density map B; and calculating a function value of the corresponding loss function to obtain a corresponding weight value. The method has the remarkable effects that the probability-based deep learning technology is used for image training, so that the number of birds is estimated.

Description

technical field [0001] The invention relates to the technical field of bird recognition, in particular to a deep learning and recognition method for dense bird flocks. Background technique [0002] In many wild bird sanctuaries in my country, flocks of birds spend the winter, reproduce and inhabit there every year. They of the same species are often densely packed and crowded together to rest, forage, talk, fly, and sometimes fill the sky. Tracking and counting of birds has become an unavoidable problem in nature reserves. [0003] The defect of the prior art is that traditional statistical methods are basically unable to identify and count dense flocks of birds, and people often try to count them by taking photos. Due to the large number of photos and the very dense data of the birds in the photos, the counts displayed are basically unrealistic. In recent years, deep learning algorithms have been used in bird recognition, and they mostly count birds in photos for statist...

AI Technical Summary

Problems solved by technology

However, when the flocks of birds are too dense, due to the mutual occlusion between the flocks of birds, it is impossible to achieve correct identification. Even manual segmentation is very difficult, and the existing machine segmentation is basically impossible.

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