drivable-area-segmentation
There are 2 repositories under drivable-area-segmentation topic.
- Language:Python 1845
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。
Language:Python 250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。
Language:C++ 63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。
Language:C++ 12balnarendrasapa / road-detection
This is a course project for DSCI-6011 - Deep Learning. deals with Drivable Area and lane segmentation for self driving cars
Language:Jupyter Notebook 4harrylal / 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
Language:C++ 4christofel04 / Argoverse-API-and-Visualization-for-3D-Drivable-Road-Detection
Argoverse API for manipulating Argoverse 1 and Argoverse 2 Dataset for 3D Drivable Area Detection using LiDAR
Language:Jupyter Notebook 3An 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
Language:C++ 1ntkhoa95 / 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".
Language:Python
