Detection and classification of vehicle types using machine learning technology

Abstract

In this paper, we focus on detection and recognition of vehicles from a video stream. Contrasted with conventional techniques for article identification and arrangement, Machine learning strategies are another idea in the field of PC vision. Our model works in two phases: an information planning step, it comprises of applying Treatments on the pictures forming the dataset so as to separate the qualities, the subsequent advance is to apply the idea of convolutional neural systems to order vehicles. Vehicle discovery permits the utilization of different uses of computerized reasoning framework for a few purposes, particularly: canny transportation, programmed checking, self-sufficient driving, and driver wellbeing ensure. The motivation behind this article is to enable us to identify vehicles moving before us by means of a camera put under the rearview mirror and draw the direction lines of our vehicle. In this work, we center on the location and acknowledgment of vehicles in a video stream. We have demonstrated that our strategy for work extraordinarily improves the exactness rate and diminishes the mistake rate, however in spite of the utilization of regularization, institutionalization and advancement systems, the preparation time of our model remains an issue to raise. Our method gave better results in terms of precision, detection and classification where we obtained an accuracy of 99.2%.