Detection and Classification of Vehicle Types Using Machine Learning Technology

Aug 5, 2026

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Vehicle detection and recognition from continuous video streams is addressed using machine learning, positioning convolutional neural networks as the core approach. The workflow operates in two phases: dataset information preparation through image transformations, followed by CNN-based ordering of vehicle instances. The study targets practical scenarios such as intelligent transportation, automatic monitoring, autonomous driving, and driver safety. Results show improved precision and reduced error rates, with an achieved accuracy of 99.2%, while training time remains a key issue despite regularization, standardization, and optimization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Background

Detection and classification of vehicle types using machine learning technology Mohamed Adel Al-Shaher Computer Science-Information Technology, College of Science; University of Thi-Qar, Nassiriyah, Iraq Article Info ABSTRACT | Received Feb 25, 2019 | 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, selfsufficient 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% . | | --- | --- | | Keyword:
Vehicle Detection Recognition Machine Learning Classification Pre-trained Models Deep Learning | | Corresponding Author: Dr. Mohamed Adel Al-Shaher Computer Science-Information Technology College of Science University of Thi-Qar , Nassiriyah; Iraq Email: [alshaher_comp82@sci.utq.edu.iq](alshaher_comp82@sci.utq.edu.iq) ; [alshaher2006@yahoo.com](alshaher2006@yahoo.com) 1. Introduction In this paper, vehicle detection allows the use of various applications of artificial intelligence system for several purposes, especially intelligent transportation, automatic monitoring, autonomous driving, and driver safety guarantee. The purpose of this article is to allow us to detect vehicles moving in front of us via a camera placed under the rearview mirror and draw the trajectory lines of our vehicle. In this work, we focus on the detection and recognition of vehicles in a video stream. For this reason, we have used the convolutional neural network technique and a dataset that contains images to enable recognition and classification of vehicles. The main purpose of our work is therefore to reduce human effort and offers research perspectives to make driving more enjoyable and almost autonomous. Our paper is organized as follows: the next part will discuss previous work that uses learning methods for vehicle detection, then we will explain our proposed architecture (model), then we will calculate the estimate of the deviation of the vehicle, in the following part we will present the results obtained from this research. Finally, we will finish our work with a conclusion, and we will propose perspectives. Figure 1. Basic 3x3 convolving filter on a minor region of image. 2. Background Convolutional neural networks are inspired from the visual brain cortex; they are used in recognition systems, robotics and self-driving cars as well as in other areas. They provide good results and a higher accuracy rate compared to traditional methods. A vehicle detection method presented by Chabot. F and al whose goal is to recognize the brand and model of vehicles. This approach is based sure setting correspondence between the vehicle in the picture and real 3D model. This method is based on a detector based on convolutional neural networks (CNN); its principle

FAQs

What is the main objective of the proposed method?

To detect and recognize vehicles in a video stream, enabling subsequent classification based on visual input.

How does the model work at a high level?

It uses a two-phase process: dataset preparation with image transformations, then convolutional neural network modeling to identify and order vehicles.

What performance results are reported?

The method improves accuracy and reduces error rate, achieving 99.2% accuracy for detection and classification.

Aug 5, 2026

Upload by Aurelia