Design And Development Of Trunk Detection System Based On Deep Learning

In recent years,with the rapid advancement of computer vision technology,deep learning has made tremendous changes in many fields.It has been used in speech recognition,natural language processing,computer vision,image and video analysis,multimedia,etc.The applications have achieved great success.On the basis of images,deep learning has important significance in identifying tree species,protecting tree species diversity,and strengthening information management of agriculture and forests through tree image recognition.The processing and analysis of tree trunk images taken by different equipment under natural light has become one of the main research topics of tree recognition,which is also the main content of this paper.In order to achieve the accuracy and robustness of the algorithm for the recognition of trunk images from different sources,this article uses a variety of smart mobile devices to collect trunk images,complete the construction of the data set,and perform the necessary preprocessing operations on the trunk images,including color correction and trunk image segmentation.Finally,the pre-processed trunk image is classified and identified and the trunk diameter is calculated.The main research work of this article is as follows:Trunk image segmentation: divide into color correction and trunk image extraction.Firstly,several existing color correction methods are analyzed.According to the color distribution of trunk images,a trunk image correction method based on RGB color space is selected.Secondly,based on the characteristics of the trunk image,based on the use of deep learning algorithms,the full convolutional neural network algorithm is used for trunk segmentation,and an optimization algorithm based on morphology is designed to further process the segmentation results.Achieve robust trunk splitting.Trunk image recognition: first analyze the trunk image,analyze the classification problem of the trunk image,introduce a convolutional neural network for feature classification for this problem,and design the network with deep learning as the main idea.On this basis,in order to further the department improved the performance of the system and adjusted some parameters during the training of the network.The experiment based on deep learning tree image recognition shows that the joint training method of loss function can further improve the recognition accuracy while ensuring that the prediction time of the algorithm remains unchanged.Tree diameter calculation: use the edge detection operator to obtain the outline of the trunk,and then calculate the diameter of the trunk based on the scale factor and the principle of small hole imaging and the relevant parameters measured by the sensor.Through the trunk diameter,the approximate age of the tree can be analyzed.On the basis of completing color correction,trunk extraction,tree diameter calculation,and trunk recognition,this paper develops an image detection platform based on the web,designs and implements the system based on a lightweight recognition model,and uses a separate front and back end to design images on the web Segmentation,identification and diameter measurement system.