Classification Of Digital Images Using MobileNet Architecture, Local Binary Pattern-Support Vector Machine (LBP-SVM), And Local Binary Pattern-Random Forest (LBP-RF)
In recent developments, the presence of machine learning and deep learning in object classification in computer vision has brought about significant discoveries. One implementation of classification in computer vision is the classification of infrastructure conditions, in this case, concrete cracks. Digital image modeling can be done using several models, including Convolutional Neural Network architecture, Support Vector Machine (SVM), and Random Forest. This research was conducted to examine the performance of Convolutional Neural Network, Support Vector Machine (SVM), and Random Forest (RF) models in classifying digital images of concrete cracks. In this research, Local Binary Pattern (LBP) feature extraction is used for SVM and RF models, while MobileNet architecture is used for CNN because it has the advantage of low computational cost on devices with limited specifications. The classification performance in this research is measured using accuracy and F1 score. The result shows that the best classification performance among the MobileNet CNN, SVM, and RF models is achieved by the MobileNet CNN model with a width multiplier of 0.77, having the highest accuracy and F1 score of 99.8875% and 0.998875, with an inference time of 0.045502 (images/second). The Local Binary Pattern (LBP) feature extraction best model is obtained by RF with splitting rule Information Gain, num points=48, and radius=5, with an accuracy and F1 score of 97.775% and 0.97775 with inference time of 0.047936 (images/second). The LBP on the SVM model dan achieve accuracy and F1 score of 95.8624% and 0.958625, with inference time of 0.009384 (images/second) with parameters num points=24, radius=5, and polynomial kernel.
Keywords: Digital Image Classification, MobileNet Convolutional Neural Network, Support Vector Machine (SVM), Random Forest (RF), Local Binary Pattern (LBP)
- S-1-2024-462305-abstract.pdf
- S-1-2024-462305-bibliography.pdf
- S-1-2024-462305-tableofcontent.pdf
- S-1-2024-462305-title.pdf
- for other file contact the author at : ahmad.habib.hasan@mail.ugm.ac.id or ahmad.habib.hasan.zein@gmail.com
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