One of the most significant issues in modern road safety and intelligent transportation systems
is the automation of vehicle detection and identification. Many challenges have been solved in
the advancement of image processing, pattern recognition, and deep learning technology in
order to accomplish this goal. Vehicle Type Classification is a difficult task since the dataset
has a large class imbalance, and several view- points for different cars can be identical. The
proposed framework employs a shallow Convolutional Neural Networks (CNN) architecture
to prevent overfitting and ensure that the correct features are learned, and we use an
augmentation technique to produce synthetic images using the image data generation model in
Keras due to class imbalance. The shallow CNN is used to extract features from the generated
images, and then Softmax activation is used to classify them. Finally, the proposed system will
achieves the classification of vehicle type i.e. classify the different car models with efficiently
by novel methodology. The findings of the experiments demonstrate that shallow CNN can do
well in real-world situations.