This is one of the best vehicle recognition applications. It can determine the car's license plate number, color, model, brand and year.

Python scripts for performing road segemtnation and car detection using the HybridNets multitask model in ONNX.

In this system you can detect cars from video or live webcam

In this project, we compared different YOLO models by training them on drone images from the Unifesp parking lot to detect cars. Our objective was to assess their performance and identify the most effective model for improving traffic flow and optimizing parking space utilization.

使用OpenCV部署HybridNets，同时处理车辆检测、可驾驶区域分割、车道线分割，三项视觉感知任务，包含C++和Python两种版本的程序实现。本套程序只依赖opencv库就可以运行， 彻底摆脱对任何深度学习框架的依赖。

This app detects types of cars and counts cars using YOLOv3

detecting model and the name of the cars with deep neural networks like VGG-16 , YOLOv5 and YOLOv8

使用HOG和SVM进行目标检测

YOLOPv2のPythonでのONNX推論サンプル

Iranian Vehicle Tracking and Recognition + License Plate Recognition Using Yolov5, OSNet

MASK-RCNN implementation for Lyft Perception Challenge

This program detects vehicles that are not moving

Module for car detection

A simple night time vehicle tracking algorithm via headlight

This project utilizes the custom object detection model to monitor parking spaces in a video feed. It identifies vehicles in the video and overlays polygons representing parking spaces on the frames. The program then calculates the number of occupied and free parking spaces based on the detected vehicles and the predefined parking space polygons.

Application of pre-trained YOLO object detection model to car detection for autonomous driving

software pipeline to identify the lane boundaries and cars in a video for autonomous self-driving cars

This project is for learning computer vision using openCV and python.

Object classification and segmentation in images