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For the crack width recognition task, the random forest algorithm yielded an accuracy of 97.2%, a sensitivity of 100%, a specificity of 85.7%, and a TPR of 100%. All the tests were carried out in the same way as for the crack orientation and crack length recognition tasks.
For the crack width classification task, the random forest algorithm yielded an accuracy of 97.2%, a sensitivity of 93.3%, a specificity of 100%, and a TPR of 100%. All the tests were carried out in the same way as for the crack orientation and crack length recognition tasks.
The results show that the crack recognition and crack width classification models are comparable in terms of the error rate, although the crack recognition model has slightly better recall. The confusion matrix shows that the crack recognition model has a slightly better performance for both the crack and the type of crack recognition. The crack recognition model achieved about the same performance in the presence of the MAV feature with left and right displacements combined with proximity data, and the crack width classification model performed almost as well, with an error rate of about 0.01%, which indicates that the crack width could be identified, even for complex cracks. The crack recognition model achieved a much better performance when using only the crack width feature and left and right displacements with proximity data, and the crack width classification model achieved a performance similar to that of the crack recognition model. The results also show that the crack recognition model has an error rate for the crack width classification of about 8%, whereas the crack width classification model has an error rate of about 3%.
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