Cattle Body Intelligent Measurement based on Improved CenterNet

Abstract

Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.