A CNN Model for ODOL Truck Detection
DOI:
10.33395/sinkron.v8i3.13780Keywords:
CNN, Odol, Truck, Non-Odol,Artificial IntelligenceAbstract
This study developed a Convolutional Neural Network (CNN) model as one of artificial intelligence method to detect trucks experiencing over-dimension and over-loading (ODOL). The primary goal of this research is to enhance the efficiency of truck monitoring, reduce road infrastructure damage, and support the sustainability of transportation using artificial intelligence approaches. The model was trained using a dataset consisting of ODOL and non-ODOL truck images, and successfully achieved a testing accuracy of 94.23%. The confusion matrix analysis demonstrated the model's ability to classify trucks with high precision. Additional testing on truck images not included in the training or testing dataset showed the model's potential for good generalization.
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Copyright (c) 2024 Nurul Afifah Arifuddin, Kharisma Wiati Gusti, Rifka Dwi Amalia
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