中圖分類號(hào)：TP391.41 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.256569 中文引用格式： 沈域，徐愛(ài)俊，周素茵，等. 基于雙視角點(diǎn)云配準(zhǔn)的豬只體尺測(cè)量方法研究[J]. 電子技術(shù)應(yīng)用，2025，51(11)：35-45. 英文引用格式： Shen Yu，Xu Aijun，Zhou Suyin，et al. Research on pig body size measurement method based on dual view point cloud registration[J]. Application of Electronic Technique，2025，51(11)：35-45.
Research on pig body size measurement method based on dual view point cloud registration
Shen Yu1，Xu Aijun1，2，Zhou Suyin1，Ye Junhua3
1.College of Mathematics and Computer Science， Zhejiang A&F University； 2.Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture； 3.College of Environment and Resources， Zhejiang A&F University
Abstract： Body size parameters are critical indicators in pig breeding. A non-contact pig body size measurement method using dual view point cloud registration was proposed to address the isssues of single parameter, complex equipment, and limited processing of large-scale point cloud data exsited in current pig body size measurement methods. Firstly, we constructed a data acquisition system with two Kinect DK depth cameras to collect bilateral point cloud data of pig and then we preprocessed the data using an improved Local Outlier Probability (LoOP) filtering algorithm and a multi-level feature extraction method for point cloud simplification. Secondly, the dual view point cloud registration was completed by combining coarse registration and fine registration algrithms. Finally, We integrated normal vector point cloud with the Alpha Shapes algorithm to extract pig contour features of pig, achieving non-contact measurement of multiple body size parameters. The experimental results showed that the average relative errors of pig body length, body height, hip height, body width, abdominal width, hip width, chest circumference, and abdominal circumference were 1.28%, 0.88%, 1.97%, 2.71%, 2.83%, 3.71%, 2.03%, and 2.17%, respectively. The overall average relative error and absolute error were 2.20% and 1.04 cm, respectively. The method in this study provides an accurate, non-invasive solution for multi-parameter measurement of pig, offering technical support for efficient breeding selection in pig farming.
Key words : pig；dual view；depth cameras；point cloud；body size measurement
