Classification of Vehicle Types With Deep Learning Based on Image Processing


  • Alesia Lorenzza Sinaga Potensi Utama University
  • Wanayumini Potensi Utama University
  • Rika Rosnelly Potensi Utama University



Convolutional Neural Network, Deep Learning, Image, Support Vector Machine, Vechile


Vehicles play a crucial role in facilitating road traffic and transportation, serving as a primary mode of transportation for people in their daily activities. Various types of vehicles, such as cars, motorcycles, and buses, are commonly utilized for longdistance travel. This study employs Convolutional Neural Network (CNN) and Support Vector Machine (SVM) methods to enhance accuracy in image classification by adjusting the number of epochs and increasing the size of the training dataset. Utilizing a size of 128x128 pixels, the CNN method achieved the highest accuracy rate at 99.33%, surpassing SVM's accuracy rate of 85.06%. Consequently, The Convolutional Neural Network (CNN) method has emerged as the superior choice Compared to the Support Vector Machine (SVM) for image classification tasks.