基于U-Net的粮仓储粮高度定量检测方法  

Quantitative Detection Method of Grain Storage Height in Grain Silos Based on U‑Net

作　　者：任飞燕 张蕙 李智 杨卫东[1] 张艳飞 陈卫东 谈云建[4] 柳瑞芸 REN Feiyan;ZHANG Hui;LI Zhi;YANG Weidong;ZHANG Yanfei;CHEN Weidong;TAN Yunjian;LIU Ruiyun(Henan Province Key Laboratory of Grain Photoelectric Detection and Control,Zhengzhou 450001,China;College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China;School of Food and Strategic Reserves,Henan University of Technology,National Engineering Research Center of Grain Storage and Logistics,Zhengzhou 450001,China;Huaxin Consulting Design and Research Institute Co.,Ltd.,Hangzhou 310051,China)

机构地区：[1]河南省粮食光电探测与控制重点实验室,河南郑州450001 [2]河南工业大学信息科学与工程学院,河南郑州450001 [3]河南工业大学粮食和物资储备学院/粮食储运国家工程研究中心,河南郑州450001 [4]华信咨询设计研究院有限公司,浙江杭州310051

出　　处：《河南农业科学》2024年第11期156-163,共8页Journal of Henan Agricultural Sciences

基　　金：河南省杰出青年基金项目(222300420004)。

摘　　要：我国储粮分布具有点多、线长、面广的特点,传统的粮食数量监管面临着效率低、成本高、严重滞后等问题,亟需研发可对粮仓储粮数量实时快速检测的新技术。针对该问题,提出一种基于U-Net的粮仓储粮高度定量检测方法,通过对仓内监控相机拍摄的图片中粮面和通风窗进行分割处理,得到粮面边缘和通风窗上下边缘像素值,并以通风窗上下边缘的离地高度为基础,计算得到粮仓储粮的实际高度,再结合粮仓长度、宽度和粮食密度等基础数据,即可得到粮仓储粮实际数量。分别使用U-Net、DeepLabV3+和PSPNet 3种算法对分割后的粮仓图片进行分析处理并计算储粮高度。结果表明,U-Net的平均交并比(MIoU)和平均像素准确度(MPA)分别为93.25%和95.88%,MIoU相较于DeepLabV3+和PSPNet分别提高1.82、2.69百分点,MPA相较于PSPNet提高2.42百分点;U-Net的定量分析误差为3.51%,比DeepLabV3+和PSPNet分别低1.34、0.43百分点,较为适合作为粮仓储粮高度定量计算分割算法。该方法无需提前设置测量标尺,仅依靠仓内监控相机即可实现粮堆高度测量,可基本满足粮仓储粮数量的快速检测需求。Due to the distribution of grain storage in China is characterized by many points,long lines and wide areas,the traditional method of monitoring grain quantity is struggling with low efficiency,high costs,and significant delays.There is an urgent need to research and develop a new technology that can perform real‑time and rapid detection of the quantity of grain stored in grain silos.To address the problem,we proposed a quantitative detection method of grain storage height in grain silos based on U‑Net.By segmenting the grain surface and ventilation windows in pictures captured by monitoring cameras in the grain warehouse,we obtained pixel values of the edge of the grain surface and the upper and lower edges of the ventilation window.Taking into account the height of the ventilation window’s upper and lower edges above and below the ground,we could determine the actual height of grain storage in the grain silo.Subsequently,merged with the length,width,grain density,and other fundamental data,we obtained the actual quantity of grain stored in the grain silo.In this study,the height of grain storage was calculated by analyzing and processing the segmented soil images using U‑Net,DeepLabV3+,and PSPNet algorithms respectively.The experimental results showed that the mean intersection over union(MIoU)reached 93.25%,which was 1.82 and 2.69 percentage points higher than that of DeepLabV3+and PSPNet,respectively.The mean pixel accuracy(MPA)reached 95.88%,which was 2.42 percentage points higher compared with PSPNet.The quantitative analysis error of U‑Net was 3.51%,which was 1.34 and 0.43 percentage points lower compared with DeepLabV3+and PSPNet,respectively.U‑Net was more suitable as a segmentation algorithm for quantitative calculation of grain storage height in granaries.It is not necessary to set up the measuring scale beforehand and with this method.By relying solely on the monitoring camera inside the warehouse,the height measurement of grain pile can be achieved,which can effectively meet the fast dete

关 键 词：粮仓储粮数量 储粮高度 语义分割 U-Net 定量分析 计算机视觉 

分 类 号：S126[农业科学—农业基础科学]