This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation

The deep semantic segmentation network for lane segmentation.

Example scripts for the detection of lanes using the ultra fast lane detection model in Pytorch.

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

Lane Segmentation powered by InSPyReNet (ACCV 2022).

Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

Drive a RC car on a track, detecting lanes using semantic segmentation. Uses Donkeycar, ROS, Raspberry Pi 4, Google Edge TPU.

YOLOPv2のPythonでのONNX推論サンプル

A model on lane detection has been developed with the data set we have created.

YOLOTL: YOLO-based Top-view Lane Segmentation and Steering Control

The deep semantic segmentation network for lane segmentation.

Dataset for training lane segmentation and traffic sign detection from Goodgame, the Champion of FPT Digital Race - Cuộc Đua Số 2020.

Right lane segmentation in Duckietown environment

Python script for vehicle detection and tracking using YOLOv8 with dynamic road extent visualization. The model adapts to various camera angles, accurately determining road height by detecting road, sky, and mid lines. Includes TTC calculations, cut-in warnings, and visual overlays.

VisionDriveX is a multi-task autonomous driving perception system that performs traffic sign classification, stop-sign detection, and lane segmentation. Built with PyTorch and explainable AI (Grad-CAM), it delivers real-time, interpretable road understanding for safety-critical ADAS applications.

🚗 Enhance autonomous driving with VisionDriveX, a perception system for traffic sign classification, stop-sign detection, and lane segmentation, featuring explainable AI.