摘要
[目的/意义]牛的体尺参数是反映牛身体发育状况的关键指标,也是牛选育过程的关键因素。为解决规模化肉牛牧场复杂环境对肉牛体尺的测量需求,设计了一种图像采集装置以及体尺自动测量算法。[方法]首先搭建肉牛行走通道,当肉牛通过通道后进入限制装置,用英特尔双目深度相机D455对牛只右侧图像进行RGB与深度图的采集。其次,为避免复杂环境背景的影响,提出一种改进后的实例分割网络Mask2former来对牛只二维图进行前景轮廓提取,对轮廓进行区间划分,利用计算曲率分析方法找到所需体尺测点。然后,将原始深度图转换为点云数据,对点云进行点云滤波、分割和深度图牛只区域的空值填充,以保留牛体区域的点云完整,从而找到所需测点并返回到二维数据中。最后,将二维像素点投影到三维点云中,利用相机参数计算出投影点的世界坐标,从而进行体尺的自动化计算,最终提取肉牛体高、十字部高、体斜长和管围4种体尺参数。[结果与讨论]改进的实例分割网络与Mask R-CNN、PointRend、Queryinst等模型相比具有更好的分割结果。采用本研究测得的这4种体尺平均相对误差分别为4.32%、3.71%、5.58%和6.25%。[结论]本研究开发的肉牛图像采集装置及相应的图像处理方法可以满足该牧场对肉牛体尺无接触自动测量误差小于8%的精度要求,为非接触式肉牛体尺自动化测量提供了理论与实践指导。
[Objective]The body size parameter of cattle is a key indicator reflecting the physical development of cattle,and is also a key factor in the cattle selection and breeding process.In order to solve the demand of measuring body size of beef cattle in the complex environment of large-scale beef cattle ranch,an image acquisition device and an automatic measurement algorithm of body size were designed.[Methods]Firstly,the walking channel of the beef cattle was established,and when the beef cattle entered the restraining device through the channel,the RGB and depth maps of the image on the right side of the beef cattle were acquired using the Inter RealSense D455 camera.Secondly,in order to avoid the influence of the complex environmental background,an improved instance segmentation network based on Mask2former was proposed,adding CBAM module and CA module,respectively,to improve the model's ability to extract key features from different perspectives,extracting the foreground contour from the 2D image of the cattle,partitioning the contour,and comparing it with other segmentation algorithms,and using curvature calculation and other mathematical methods to find the required body size measurement points.Thirdly,in the processing of 3D data,in order to solve the problem that the pixel point to be measured in the 2D RGB image was null when it was projected to the corresponding pixel coordinates in the depth-valued image,resulting in the inability to calculate the 3D coordinates of the point,a series of processing was performed on the point cloud data,and a suitable point cloud filtering and point cloud segmentation algorithm was selected to effectively retain the point cloud data of the region of the cattle's body to be measured,and then the depth map was 16.Then the depth map was filled with nulls in the field to retain the integrity of the point cloud in the cattle body region,so that the required measurement points could be found and the 2D data could be returned.Finally,an extraction algorithm was designed to combine
作者
翁智
范琦
郑志强
WENG Zhi;FAN Qi;ZHENG Zhiqiang(School of Electronic Information Engineering,Inner Mongolia University,Huhhot 010021,China;State Key Laboratory of Reproductive Regulation&Breeding of Grassland Livestock,Huhhot 010010,China)
出处
《智慧农业（中英文）》
CSCD
2024年第4期64-75,共12页
Smart Agriculture
基金
国家自然科学基金项目(61966026)
内蒙古自治区高等学校青年科技英才支持计划(NJYT23063)
内蒙古自然科学基金项目(2021MS06014)。
