SNE-RoadSeg for Freespace Detection in PyTorch, ECCV 2020
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Updated
Oct 25, 2024 - Python
SNE-RoadSeg for Freespace Detection in PyTorch, ECCV 2020
Deep Learning based Road Segmentation using Satellite Imagery
A U-Net-50 model for segmenting road networks in aerial images. Trained on the Massachusetts Roads Dataset, the model accurately identifies and segments roads, making it ideal for applications in urban planning and autonomous driving
we introduce R2S100K---a large-scale dataset and benchmark for training and evaluation of road segmentation in challenging unstructured roadways.
Semantic segmentation of road networks in high-resolution satellite images using a deep neural network architecture.
Development for my M.Tech Thesis: Deep Learning Techniques for Road Segmentation in Indian Context
This dataset contains 2,224 images captured within the LUMS campus, each manually annotated for 5 classes. Ideal for training semantic segmentation models for road detection.
🌱 SNE-RoadSeg in PyTorch, ECCV 2020 by Rui (Ranger) Fan & Hengli Wang, but now we have improved it.
Graph Reasoned Multi-Scale Road Segmentation in Remote Sensing Imagery
🚘 Segmentação de faixas de estrada com U-NET DNN.
Implementation of object detection and semantic segmentation of traffic objects in the front facing car camera using OpenVINO's pretrained models.
Multi-Modal Multi-Task (3MT) Road Segmentation, IEEE RA-L 2023
2D road segmentation using lidar data during training
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗
This is my bachelor's thesis, which contains three main features: lane detection, road segmentation, and a Forward Collision Warning (FCW) system
A guide to use MMSegmentation to develop and benchmark different models on custom dataset.
Identification of road surfaces and 12 different classes like speed bumps, paved, unpaved, markings, water puddles, potholes, etc.
YOLOPv2のPythonでのONNX推論サンプル
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。
A course project for road segmentation using a U-Net Convolutional Neural Network on the KITTI ROAD 2013 dataset
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