Classification Techniques Applied in Wood Logs Tomographic Image


The forestry assessment field uses the Non-Destructive Tests (NDTs) to analyze woods logs. To assist the identification of anomalies inside the trunks, the ultrasonic tomography can be used as an alternative. With this technique is possible to evaluate the internal conditions of wooden trunks, through wave propagation applied at specific points. To help the identification and analysis of defects in the tomographic image, this work uses resources from the area of Machine Learning approach in order to identify tomographic images with anomalies. In this study is considered three different classifiers: K-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The experiments performed were compared using metrics: Accuracy, Precision and Recall. To carry out the experiments, a dataset with 5000 ultrasonic tomography images was build using the Data Augmentation process. In the first experiment, the metrics are calculated based on texture descriptors. The best accuracy results obtained for the CNN, SVM and k-NN models were respectively 89.00%, 80.70% and 79.81%. In the second experiment, the anomaly segmentation was performed with Otsu’s segmentation, then it was tested in the SVM classification model. The results found that the SVM model has superior results of demarcation when compared to the CNN model.
Palavras-chave: Ultrasonic tomography, Non-Destructive Tests, Wood defects


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PEREIRA JUNIOR, Antonio Alberto; CARVALHO, Marco Antonio Garcia de. Classification Techniques Applied in Wood Logs Tomographic Image. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 63-69. DOI: