ObjectiveThere are intra-class differences and inter-class similarities in tree species image recognition, which makes it difficult for traditional methods based on single artificial features to achieve ideal recognition results. In order to solve these problems, a tree image depth learning recognition method based on convolution neural network was proposed, which combines deep features of the image with artificial features.
MethodSix kinds of common tree species, including Pinus sylvestris var. mongolica, Populus davidiana, Betula platyphylla, Larix gmelinii, Cedrus deodara and Pinus alba, were studied. Firstly, the original tree species image set was expanded by clipping, horizontal flipping, rotation and other operations, and was divided into training set and test set to establish the image database of this tree species recognition experiment; secondly, the model was designed as three parallel channels. The network selected RGB image, HSV image and LBP-HOG image, respectively, and recognized the above tree image from the point of view of pixel value, color, texture and shape. On the one hand, a CNN depth learning model suitable for this experiment was constructed. The RGB image and the corresponding HSV image in the training set were used as the input of the first and second CNN models to extract the deep features of tree image. On the other hand, the training set was de-noised by Gaussian filtering, and LBP-HOG features were extracted artificially to represent texture and shape features as the input of the third CNN model. Finally, the features obtained by each of the three models were summarized in the last layer of the fully connected layer as the final classification basis of the soft Max classifier. Finally, in order to verify the feasibility of the proposed method, the SVM classifier, BP neural network, the existing depth learning LeNet-5 model and VGG-16 model were trained by the above features and training set, and the test set was identified and verified to compare the final recognition effect.
ResultThe training accuracy of the multi-feature fusion CNN model was 96.13%, and the average recognition accuracy was 91.70%. In the CNN tree species recognition model based on one-way training, the recognition rate of RGB image as training input value was the highest, which was 75.21%, followed by HSV feature recognition rate, and LBP-HOG feature was the worst; in the case of multi-feature fusion, the combination recognition rate of RGB + HSV + LBP + HOG was the highest, which reached 93.50%; in the case of RGB + H channel + LBP + HOG, the recognition rate of RGB + HSV + LBP + HOG was the highest. The recognition rate was 89.50%. Under the same condition of feature or feature combination, the recognition rate of SVM, BP neural network, LeNet-5 model and VGG-16 model was lower than that of the model in this paper.
ConclusionBased on RGB + H channel + LBP feature fusion, the three-way parallel CNN model is used to get the highest recognition rate for the six types of tree images in this paper, which overcomes the problem of low recognition rate in the case of a single feature, and the recognition effect is also very ideal. It realizes automatic recognition of specific categories from a large number of different tree images.