Dynamic Monitoring Methodology of Grain Quantity Variation in Granaries Based on Semantic Segmentation
In order to efficiently,accurately and cost-effectively monitor changes in the quantity of grain in grain warehouses.In this paper,a dynamic monitoring model was presented for grain storage based on semantic seg-mentation.In this approach,deep learning techniques were utilized to analyze and process images collected by cam-eras inside the granary,enabling the dynamic monitoring of the changes in grain quantities inside the granary.Final-ly,the monitoring results were compared with recent business data to identify any illegal behavior during routine su-pervision and provide timely feedback to the grain storage regulators.The proposed approach enhanced the targeting and efficiency of grain inventory inspections.In this paper,the images collected by the monitoring camera in the granary were selected as the data set,and a dynamic monitoring model of granary grain quantity change based on DeepPlabv 3+was constructed.By extracting the reference boundary to judge the grain surface change,the change of grain quantity in the granary was judged by the change of the pixel value of the reference boundary,and by intro-ducing the feature extraction network based on MobileNetV2,the accuracy and calculation efficiency of model identi-fication were improved.The experimental results indicated that this model had the mean intersection over union and mean pixel accuracy reaching 89.57％and 94.53％,respectively,with the number of parameters of 5.818 M.This model improved the Mean Intersection over Union by 0.95％and 0.88％compared to PSPNet and UNet models,re-spectively.Through the testing analysis of 50 grain silos,the consistency between the change of grain quantity in the silo obtained by the model identification and the actual situation was 96％,demonstrating the effectiveness of this method and providing a new idea for the dynamic monitoring of grain quantity in grain silos.
deep learningDeepLabV3+grain surface recognitionsemantic segmentation
NETL
NSTL
万方数据
