drivable-area-segmentation 开源项目推荐（9 个）

hustvl /YOLOP

用于全景驾驶感知的 You Only Look Once。（MIR2022）

hpc203 /YOLOP-opencv-dnn

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。

hpc203 /yolopv2-opencv-onnxrun-cpp-py

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。

hpc203 /hybridnets-opencv-dnn

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。

harrylal /TwinLiteNet-onnxruntime

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

balnarendrasapa /road-detection

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

harrylal /TwinLiteNet-onnx-opencv-dnn

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

CoNfIg7952 /UNet_Drivable_area_Segmentation

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

ntkhoa95 /Self-Supervised-Label-Generator

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".