The processing of the one board takes a long time, even when using architectures with less number of parameters. A comparison was made to determine how the number of patches per board affects the accuracy of the identification of the entire board. The experiment was conducted on the same test set as in the previous experiments, consisting of 177 boards. Figure 30 shows the mean accuracy along the y axis and the number of used image patches alongxaxis. Total number of image patches extracted is 250 on the average. The experiment was performed for the 50 of used patches. For each value of used patches the experiment was repeated 100 times and the mean of obtained accuracies were computed.
The experiment is performed with the dataset containing all image patches. As the result the experiment showed that it can be used only 25 image patches per board for each architecture to obtain high accuracy. Table 8 represents the accuracy and the inference time of the board identification in case of the use only 25 image patches per one board.
Figure 30.Influence of number of image patches usage on the board identification accuracy. The figure indicates the mean accuracy that can be obtained. Total number of image patches extracted from one board is 250 on the average.
Table 8.The accuracy and the inference time in case of the use only 25 image patches per board.
Architecture ACC τB
AlexNet 0.986 0.26
VGG-16 0.721 0.72
GoogLeNet 0.994 0.47
ResNet-50 0.780 0.95
6 DISCUSSION
Wood species identification is important part of the sawmilling process. Accurately iden-tified species of wooden material allow to perform properly the technological process of the sawmill. According to the literature review, manual determination of wood species is a laborious and long procedure, requiring an experienced specialist. This serves as a huge motivation for the development of an automated approach. Also, modern approaches based on computer vision and the application of the approach using convolutional neural networks are examined.
In the work the method for wood species identification was proposed. The method in-cludes the following steps: image patches extraction, wood species identification of each image patches, and combination of the identification results to the final decision about the wood species of the whole board.
Based on the results of the work, it can be concluded that despite the presence of a large number of different parameters in the CNN architecture, their architecture templates can be selected and conduct differentiation of its parameters, highlighting the most significant.
Therefore, it is guided by the highlighted features of the architecture of the CNN and the most accurate model should be chosen for classification which is resistant to changes in the data being processed.
Based on the experimental results it can be highlighted that the best performance is shown by more simple networks with small number of layers and parameters, such as AlexNet and GoogLeNet. The accuracy of these models were 91.5% and 96.1% respectively in the single high quality image patches identification performance. The highest accuracy was indicated in board level wood species identification by the same CNN architectures AlexNet and GoogLeNet. The accuracies were 98.8% and 99.4% respectively. It was shown that simple architecture in this particular type of tasks shows more accurate results and can determine the difference in various wood species images. It should be concluded that more simple networks such as AlexNet and GoogLeNet are more suitable for the wood species identification task. The reason for this is presumably a small number of parameters in CNN allows for a smaller amount of time to reveal a significant difference between the classes of wood species. That, in turn, helps to avoid overfitting.
Also, the experiments shows that the inference time for one board can be can be short-ened by decreasing the number of used patches for identification without affection on identification accuracy. The accuracy of 99.4% can be obtained using GoogLeNet with
usage of only 25 image patches. This means it needed only 10% of total number of the image patches for one board identification. In that case the inference time for one board identification was 0.47 seconds.
6.1 Future Work
Despite the accurate performance in identification, only three wood species were used in the work. In the future work it makes sense to evaluate the work of the model in the presence of other wood species. Also, the inference speed of recognition has a strong influence, since it is an important parameter in case of application in an industrial envi-ronment. It is worth considering the possibility of accelerating the system up to real-time recognition, for example, by parallelizing of the computations. Also, the main factor is the quality of the input images. It should be considered how the resolution of the image patches affects on the identification accuracy. These measurements can show which the camera parameters and lightning conditions are more suitable for accurate wood species identification
7 CONCLUSION
In the thesis a method for wood species identification was proposed. The proposed method includes the steps of extraction of patches, their subsequent identification, and application of the decision rule to make a decision about the wood species of the whole board. In the experimental part of the work, the following architectures of convolu-tional neural networks were implemented and tested: AlexNet, VGG-16, GoogLeNet and ResNet-50. The experiments were implemented in two datasets: all image patches and only high quality ones. The experiments showed that high quality patch sorting has little impact in case of single patches identification.
The highest accuracy were shown by the AlexNet and GoogLeNet architectures in case of identification of individual patches 92.3% and 96.1% respectively. The highest accuracy is 99.4% in case of board wood species identification achieved by GoogLeNet. Also, experiments showed that the inference time for board identification with the accuracy of 99.4% can be shortened to 0.47 seconds due to the usage of only 10% of total number of the image patches for each board.
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