Grazing livestock outlier video acquisition method based on artificial intelligence and BIM

Abstract

The invention discloses a grazing livestock outlier video acquisition method based on artificial intelligence and BIM. The grazing livestock outlier video acquisition method comprises the steps of constructing a grazing area building information model; splicing the images acquired by the plurality of unmanned aerial vehicles to obtain a grazing area panorama; detecting to obtain a livestock head key point thermodynamic diagram; mapping the obtained livestock quantity information into a two-dimensional matrix to obtain a single-channel livestock quantity image; extracting features of the single-channel livestock quantity image by adopting an outlier information encoder, fusing the obtained second feature map and the first feature map to serve as input of a full connection layer, and obtaining a prediction result of whether livestock is out of group or not; and in combination with a WebGIS technology, visualizing the building information model of the grazing area. According to the invention, automatic and intelligent grazing monitoring is realized, the outlier detection efficiency is high, the detection accuracy is high, and the result is more objective.

Description

technical field [0001] The invention belongs to the technical fields of artificial intelligence, BIM, and intelligent animal husbandry, and in particular relates to an outlier video collection method for grazing livestock based on artificial intelligence and BIM. Background technique [0002] With the introduction of the concept of intelligent animal husbandry, some new grazing supervision methods have begun to appear to replace traditional human supervision methods. Some methods monitor whether any animals stray from the group in real time by installing positioning devices on the animals. The disadvantage of this method is that the cost of installing the positioning device is high, the positioning device is easily damaged, and the monitoring accuracy is low. Some methods calculate the distance between each animal and the adjacent designated area by counting the number of livestock outlines in a designated area to determine whether there is a livestock outlier. The disadva...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that, on the one hand, due to the occlusion phenomenon, the method of counting the number of livestock outlines has low precision; Dynamic delineation of designated areas, so less intelligent

Images