In recent years, Road Accidents (RAs) have emerged as an important public health issue which needs to be tackled by a multi-disciplinary approach. The trend in RA injuries and death is becoming alarming. A road traffic accident can be defined as, an event that occurs on a way or street open to public traffic resulting in one or more persons being injured or killed, where at least one moving vehicle is involved. The important factors are human errors, driver fatigue, poor traffic sense, mechanical fault of vehicle, speeding and overtaking violation of traffic rules, poor road conditions, traffic congestion, road encroachment etc. This analytical project will analyze traffic accidents more deeply to determine the intensity of accidents by using machine learning approaches. It also figures out those significant factors that have a clear effect on road accidents and provide some beneficent suggestions regarding this issue. In this project, we will be using classification methods to predict the severity of the road accidents.

This project aims to develop a machine learning model that classifies a car's make and model from an image. Using deep learning and computer vision techniques, it extracts and analyzes image features for accurate classification. Applications include automated vehicle recognition, traffic monitoring, and automotive inventory management.

Efficient vehicle classification using machine learning and deep learning models for Intelligent Traffic Systems. Classifies five vehicle types with models like SVM, Random Forest, and CNN, utilizing HOG, LBP, and Gabor features for enhanced accuracy in smart city traffic management.

It includes detection & classification of vehicles for speed limit violation and traffic rule violators driving without helmets ... In oder to accomplish this vision variuos Machine learning and Deep learning algorithm are being used.

This project is a comprehensive machine learning application that performs car feature classification using deep learning techniques. The project successfully implements vehicle detection and license plate recognition/reading using RCNN, YOLO and PaddleOCR models.

Vehicle image classification using computer vision is a popular application of deep learning and machine learning techniques. The goal of this task is to automatically classify images of vehicles into different categories, such as cars, trucks, motorcycles, and buses

Developed an end-to-end audio-based vehicle fault classification system using machine learning and deep learning models. Extracted MFCC features from engine sound data and trained Random Forest, CNN, and RNN models to classify faults into four categories. Achieved over 90% accuracy and evaluated models using classification metrics.

Detects fraudulent claims in vehicle insurance data using machine learning and deep learning. Includes data exploration, preprocessing, imbalanced data handling, and models like XGBoost, Logistic Regression, MLP, and PyTorch neural networks. Evaluates models with confusion matrices, classification reports, and cross-validation.

Image classification is a computer vision task that assigns labels to images based on their content. Using machine learning models, especially deep learning, the system learns patterns in image data to recognize and categorize objects like animals, vehicles, or scenes automatically