Vehicle Detection and Classification based on C-DSO Dataset using YOLO v3 with SRBD Method for Intelligent Transportation Applications

The tremendous evolution of Computer vision and technology, the most challenging application of vehicle category detection is an important role in our Indian Traffic system. The most of the vehicle detection algorithms facing some struggles with vehicle object detection and recognition rate. Using Deep Learning with Conventional Neural networks to undercover the problem of vehicle detection and to understand the right way of vehicle classification, detection, and vehicle recognition. This paper proposes our model of designing a dataset (C-DSO) and combines the method YOLO(You Only Look Once) V3 algorithm with SRBD to introduce the SRBD-C-DSO algorithm, it detects the smart way of integrating the pre-training of the image, labeling of vehicle image, image smoothing, and Annotation. Furthermore, renovating the dataset, adding the various categories of vehicle images, and the experimental results show a better detection rate and reduction of loss of validation in the dataset.