An easy-to-use implementation for performing inferencing with TwinLiteNet model using OpenCV DNN module. TwinLiteNet is a lightweight and efficient deep learning model designed for drivable area and lane segmentation

This repository contain code of Lane Detection and Yolo TensorRT Implementation on Autonomous Electric Vehicle

The Machathon 5.00 repository provides all the required tools and utilities for the Machathon 5.00 simulation phase.

Lane recognition system for robotic systems

Lane Detection using classical approach and a deep learning based approach

C++ programming, using OpenCV, to process and analyze a video sequence of road traffic.

Camera Aided Line Follower

Basic Vision Based Lane Detection Using OpenCV

🚘 Lane Keeping robot using an STM32, PlatformIO, and I2C communication protocol.

Lane detection is a crucial step in training autonomous driving cars. It helps identify and avoid entering other lanes by analyzing visual input. Lane recognition algorithms play a vital role in ADAS and autonomous vehicle systems. They accurately detect lane locations and borders, contributing to safe and reliable navigation.

An ESP32/Arduino + Raspberry Pi RC car featuring GPS, machine vision, pathfinding and Bluetooth control.

IOS Lane Detection with Swift and C++

Lane detection using opencv and c++

Perform inference with TwinLiteNet model using ONNX Runtime. TwinLiteNet is a lightweight and efficient deep learning model designed for drivable area and lane segmentation

simple lane detection(간단한 주행영상 차선검출 프로그램) C++ / opencv /

Detect a road lane using RGB camera

Code for the Udacity Self-Driving Car Engineer Nanodegree projects.

This is the opencv and car control dev-repo for a autonomous car controlled by a ZYBO FPGA