Species identification of wooden material using convolutional neural networks

Abstract

Wood species identification is necessary and in demand in the sawmill industry. These systems can be widely used in the control of manufacturing from raw material to the final products which increase the resource efficiency of the entire production. The main goal of this thesis is to develop a computer vision system being capable of identifying a species of the wooden material using convolutional neural networks and to determine the necessary parameters for its functioning. In the work, the method for accurate identification among three wood species was proposed. The method consists of the extraction of image patches from boards images, the wood species identification the patches using the selected convolutional neural network, and the wood species identification of each board by decision rules based on the class labelled patches. The proposed method was tested on the following convolutional neural networks: AlexNet, GoogLeNet, VGG-16, and ResNet-50. The best result were obtained using GoogLeNet architecture in that case the method was able to classify correctly 99.4% of boards in case of fir, pine, and spruce.