Study On Grape Leaves Detection And Tracking Based On Machine Vision Under Complex Background

In recent years,the cultivation of wine grape to the intensive development of large-scale cultivation to the growth of the state of the test has brought great challenges,and manual detection of low efficiency,work intensity.The health of the grape leaves is largely a reflection of the growth of the grapes,which is the primary goal of video surveillance.Real-time monitoring and tracking of leaf object is also the basis of follow-up research.For example,based on the segmentation of the machine vision,the identification of disease and the lack of defective technology need to accurately detect the blade object in complex natural background images,In addition,the leaves are growing and constantly changing their position and state.In order to determine the changes in the growth state of grapes for the leaves have been detected to be constantly tracked.In this paper,we first reconstruct the color of the degraded vane image,and then use the blade detection algorithm of the multi-angle deformable part model.Combining the color histogram to describe the appearance feature,we establish a target tracking model with deformable ability.By learning and overcoming the tracking of the target similar to the area of the shift,we achieve the grape leaf movement accurate tracking.In the aspect of blade color restoration,the image obtained by natural conditions is subjected to color distortion caused by dust interference.On the basis of atmospheric scattering model,an image color restoration model is established,and the model parameters are estimated by the dark element principle.The recovered color component approaches the original color of the blade.In the aspect of blade detection,this paper proposes a blade detection algorithm with improved hybrid multi-angle deformable part model.Firstly,the HOG feature is extracted from the G /R image,and the PCA dimensionality reduction is performed on the feature vector,which effectively eliminates the influence of illumination and background changes.Secondly,the blade detector with the front,side and back 3 angles was trained by the deformable part model.In the process of multi-model matching,the threshold and the non-maximum threshold were used to generate the leaf detection candidate set Post-processing,the overall performance of the test results have improved.The results showed that the average detection rate of leaf was 88.31% under natural conditions,the average false positive rate was 8.73%,and the accuracy of leaf detection was relatively high.In the aspect of blade tracking,this paper adopts the tracking method based on deformable model and color feature for the movement characteristics of grape leaves.In order to overcome the possibility of tracking in the target similarity region,the target interference region deformable model is established,and the model is combined with the target deformable model.In the tracking and positioning process,the maximum value of the position function is taken as the target position of the adjacent frame,and the adaptive threshold method is used to estimate the scale of the target.The accurate tracking of the blade is realized.The results show that the accuracy of blade tracking is relatively high,the overlap rate of up to 0.83,the average center error of 17.33 pixels.Finally,based on the Matlab development platform we create a visual human-computer interaction interface,achieve the monitoring of video automatic target detection and tracking,and achieve the operation by switching IP address of the different cameras.