Open AccessFast and Accurate Deep Learning Architecture on Vehicle Type Recognition
Author(s) -
Olarik Surinta,
Narong Boonsirisumpun
Publication year - 2021
Publication title -
current applied science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.14
H-Index - 3
ISSN - 2586-9396
DOI - 10.55003/cast.2022.01.22.001
Subject(s) - convolutional neural network , computer science , deep learning , artificial intelligence , license , residual neural network , image (mathematics) , pattern recognition (psychology) , data type , machine learning , computer vision , operating system , programming language
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.