drivable-area-segmentation

You Only Look Once for Panopitic Driving Perception.（MIR2022）

使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，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， 从而彻底摆脱对任何深度学习框架的依赖。

分别使用OpenCV、ONNXRuntime部署YOLOPV2目标检测+可驾驶区域分割+车道线分割，一共包含54个onnx模型，依然是包含C++和Python两个版本的程序。仅仅只依赖OpenCV就能运行，彻底摆脱对任何深度学习框架的依赖。

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 is a course project for DSCI-6011 - Deep Learning. deals with Drivable Area and lane segmentation for self driving cars

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

First 4D Radar Automatic Labelling tools using Segment Anything (SA) drivable area segmentation on camera and DA Detection using LiDAR using Deep Learning for Autonomous Vehicle

Argoverse API for manipulating Argoverse 1 and Argoverse 2 Dataset for 3D Drivable Area Detection using LiDAR

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

using the Unet model to segment images in order to find which lanes are drivable for a car

This is an unofficial Python demo of the Self-Supervised Label Generator (SSLG), presented in "Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs. Our SSLG can be used effectively for self-supervised drivable area and road anomaly segmentation based on RGB-D data".