Project on Vehicle Detection, Classification, and Counting. Done in python using OpenCV.
🤖 This repository houses a collection of image classification models for various purposes, including vehicle, object, animal, and flower classification. Each classifier is built using deep learning techniques and pre-trained models to accurately identify and categorize images based on their respective classes. Also includes a sample Flask backend!
Classifies 574 vehicle make-models using a ResNet50 architecture and YOLOv5
Traffic analysis using YOLOv4 and OpenCV
The goal of the project is to classify the vehicles, count their frequency, track them using unique ID and provide safety measures to the users by flagging the suspicious ones.
Main aim of the project is to develop a machine learning based system for detecting vehicles that drive up to the gate and to classify them into emergency services and civil cars. This may have a significant impact on the speed of gate opening and reduce the human associated problems and risks.
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.
Computer Vision Project for Vehicle Detection and Classification Dashboard of Live Singapore Traffic using YOLOv11.
8-class vehicle body type classifier using DINOv3, EfficientNet-B2, ResNet18, and MobileNetV4. Custom-curated dataset, ONNX export, HuggingFace models, Gradio and React/FastAPI demos.
Intelligent Transportation System based on Artificial Intelligence
TRI-GATE is an AI-powered automated access control system for secure vehicle entry. It combines license plate recognition, vehicle classification, and facial verification using deep learning and computer vision to ensure only authorized drivers are granted access.
A Simple Vehicle Classifier Based on Keras and Tensorflow + Training Script
Real-Time Dual-LiDAR Traffic, Vehicle Speed, Length Classification & Ferry Queue Prediction System on ESP32 & LoRaWAN
Engine identification using acoustic signal analysis and machine learning to classify 8 vehicle types. Audio signals are processed using FFT and feature extraction, and a multi-class model predicts vehicle categories based on their unique sound patterns.
A Django-based web platform that hosts multiple image classification models under one unified interface. Upload an image and get the predicted result instantly.
AI-powered traffic detection and vehicle classification system using YOLO11 for Bangladesh highway surveillance. Built with Ultralytics, OpenCV, and Python.
EfficientNet-B0 based vehicle body type classification project using PyTorch.
Vehicle type classification using Transfer Learning
CNN Vehicle Classification | PyTorch | 7 ablation experiments | 95.75% accuracy
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