Research On Rice Video Detection And Tracking Algorithm Based On Deep Learning

Food is the basis for achieving stable economic development.Food security directly affects the health of consumers and social stability.Therefore,food quality inspection is very important.Grain appearance,as an important basis for consumers to purchase,is also an important part of grain quality inspection.However,many quality inspection centers still use the traditional method of manual observation to detect the appearance of samples in the process of detecting grain.This method has not met the needs of standardization,rapidity and intelligence of grain detection in today’s society.In recent years,many scholars have carried out detection research based on image processing algorithm around the grain appearance.Although good detection results have been achieved in the end,the appearance information of a single rice needs to be extracted from the sample to the grain appearance detection.This process also needs manual auxiliary operation,which is not intelligent and automated in the process.Based on this,we explore the detection algorithm and tracking algorithm based on deep learning,and carry out research on extracting the appearance of designated grain categories in real time from the video recorded by the camera above the conveyor belt.This research is mainly divided into the following five parts:The first part describes the development status of grain detection based on visual technology at home and abroad,and determines the feasibility of this research;Then the research status of target detection and target tracking is analyzed,which provides a technical and theoretical basis for this research.The second part studies and analyzes the algorithm principles related to target detection and target tracking algorithms.By analyzing and comparing the advantages of one-stage and two-stage detection algorithms,the one-stage detection algorithm with faster detection speed is taken as the detection algorithm of this research;By combining the characteristics and application scenarios of the tracking algorithm,the sort series algorithm based on DBT strategy is taken as the tracking algorithm of this research.The third part is the research of rice video detection algorithm.Firstly,the collection platform and related data sets of rice video detection are introduced;Then the classical Yolo algorithm is compared and the optimal YOLOv5x is selected as the subsequent detection model;Based on the original YOLOv5 algorithm,the data enhancement algorithm is improved and P2 detection layer with rich details is added.The mAP0.5 and mAPall of YOLOv5 are increased to 99.8%and 92.1%respectively,further improving the accuracy of YOLOv5.The fourth part is to improve the rice video tracking algorithm.The application effect of DeepSORT tracking algorithm on rice was verified by using multiple YOLOv5 detection models.Aiming at the problem of tracking error of rice at the boundary,two methods of removing the boundary based on connected regions and detection results are proposed.Through experiments,the statistical error of rice is reduced from 4.28%to 0.17%.The fifth part summarizes the research results of the rice video detection and tracking algorithm,and looks forward to the future possible application scenarios according to the characteristics of the improved algorithm.