| With the development of intelligent and large-scale animal husbandry,compared with traditional breeding methods,intelligent breeding can improve animal health and increase breeding benefits.Moreover,the estimation of animal posture can infer the standing and lying conditions and movement duration of animals,and then make certain estimates of their living conditions and health.In recent years,depth learning has been widely used in attitude estimation.The research object is no longer limited to human,and the method of human attitude estimation can be transferred to animals.Therefore,in this paper,we use the methods of attitude estimation in recent years for reference,and introduce the depth neural network to complete the attitude estimation of animals,and conduct in-depth research on how to effectively improve the accuracy of animal attitude estimation.The research content and innovation of this paper are as follows:In view of the defects of difficult detection and missing detection in animal attitude estimation when different perspectives,key points are occluded and animal attitudes are complex and changeable,an animal attitude estimation method based on improved stacked hourglass network is designed.First of all,based on the original residual block,a large receptive field residual block and a preprocessing module are designed.Secondly,the hourglass network is designed based on the three residual blocks,and the attention mechanism is integrated into the hourglass network.Finally,intermediate supervision is set at the end of each hourglass network,and each hourglass network is cascaded using a hierarchical connection structure to form the final stacked hourglass network.Through experiments on the dataset,the experimental results show that the improved stacked hourglass network animal attitude estimation method reduces the rate of missed detection and improves the accuracy of animal attitude estimation.Aiming at the shortcomings of the network model in multi-scale feature fusion and low level small target information,an animal attitude estimation method based on improved cascaded pyramid network is designed.Compared with the CPN model,the main improvements of this method include: first,the global attention module is used to adaptively enhance the multi-scale feature map,and the full connection module is used to achieve cross scale information complementation;Secondly,Dense Net is introduced into the Refine Net part of the model,and dense connected residual blocks are designed to replace the original residual blocks,so that the feature map can be used for many times,thus increasing the diversity of features;Third,an excellent activation function Mish is introduced to optimize the model and improve the accuracy of key point positioning.The experimental results show that the improved cascaded pyramid network effectively improves the accuracy of animal attitude estimation. |
