Aiming at the complexity of image background and difficulty of feature point extraction in cattle body size measurement based on machine vision， a new cattle body size measurement method based on Kinect v4 sensor was proposed. In this method， the color and depth images were collected， and the body size data were calculated by the body feature points extracted by the combination of algorithms such as object detection， Canny edge detection， and three-point arc curvature. Firstly， an image dataset of feature parts of cattle body size was created， and the deep learning You Only Look Once v5 （YOLOv5） target detection algorithm was used to detect feature part information of cattle body size in order to reduce the interference of other parts of cattle body and background on the extraction of body size measuring points. Secondly， with the help of Canny edge detection， contour extraction and other image processing algorithms in Open source Computer Vision （OpenCV） image processing library， the key contours with measuring points of cattle body size were obtained. Then， the algorithms such as polynomial fitting and three-point arc curvature were performed on the key contours to extract the measuring points of cattle body size in two-dimensional image. Finally， the depth information was used to convert the measuring point information in two-dimensional image to three-dimensional coordinate system， and the cattle body size measurement method was designed in three-dimensional coordinate system with the RANdom SAmple Consensus （RANSAC） algorithm. Through the comparison between the experimental measurement results with the sensor and the side of cattle body at different angles and manual measurement results in a complex environment， it can be seen that the average relative error of withers height is 0.76%， the average relative error of body oblique length is 1.68%， the average relative error of body straight length is 2.14 %， and the average relative error of hip height is 0.76% in cattle body measurement data. Experimental results show that the proposed method has high measurement accuracy in complex environment.