Fast recognition system forTree images based on dual-task Gabor convolutional neural network

Aiming at the difficult problem of complex extraction for tree image in the existing complex background, we took tree species as the research object and proposed a fast recognition system solution for tree image based on Caffe platform and Dual-Task Gabor Convolutional Neural Network. In the research of deep learning algorithms based on Caffe framework, the improved Dual-Task CNN model (DCNN) is applied to train the image extractor and classifier to accomplish the dual tasks of image cleaning and tree classification. In addition, when compared with the traditional classification methods represented by Support Vector Machine (SVM) and Single-Task CNN model, Dual-Task CNN model demonstrates its superiority in classification performance. Then, for further improvement to the recognition accuracy for similar species, Gabor kernel was introduced to extract the features of frequency domain for images in different scales and directions, so as to enhance the texture features of leaf images and improve the recognition effect. The improved model was tested on the data sets of similar species. As demonstrated by the results, the improved deep Gabor Dual-Task convolutional neural network (GCNN) is advantageous in tree recognition and similar tree classification when compared with the order Dual-Task CNN classification method. Finally, the recognition results of trees can be displayed on the application graphical interface as well. Dual-Task Gabor CNN can be applied to mobile programs based on Ubantu, Android, IOS and other systems. The deep learning model used to identify tree species images can be deployed on the server side, and mobile devices can read and search for tree species images through network connections.