Vehicle Type Recognition has a significant problem that happens
when people need to search for vehicle data from a video surveillance
system at a time when a license plate does not appear in the image.
This paper proposes to solve this problem with a deep learning
technique called Convolutional Neural Network (CNN), which is one
of the latest advanced machine learning techniques. In the
experiments, researchers collected two datasets of Vehicle Type
Image Data (VTID I & II), which contained 1,310 and 4,356 images,
respectively. The first experiment was performed with 5 CNN
architectures (MobileNets, VGG16, VGG19, Inception V3, and
Inception V4), and the second experiment with another 5 CNNs
(MobileNetV2, ResNet50, Inception ResNet V2, Darknet-19, and
Darknet-53) including several data augmentation methods. The
results showed that MobileNets, when combine with the brightness
augmented method, significantly outperformed other CNN
architectures, producing the highest accuracy rate at 95.46%. It was
also the fastest model when compared to other CNN networks.
Keywords
Vehicle Type Image Recognition; image classification; Convolutional Neural Network; deep learning; pattern recognition; image recognition