There are 36 repositories under lane-detection topic.
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
Udacity Self-Driving Car Engineer Nanodegree projects.
Unofficial implemention of lanenet model for real time lane detection using deep neural network model https://maybeshewill-cv.github.io/lanenet-lane-detection/
Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)
You Only Look Once for Panopitic Driving Perception.(https://arxiv.org/abs/2108.11250)
Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)
End-to-end Lane Detection for Self-Driving Cars (ICCV 2019 Workshop)
Advanced lane detection using computer vision
Code for the paper entitled "Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection" (CVPR 2021)
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (ICCV 2017)
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, PRNet, RESA, LSTR, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help
An open source lane detection toolbox based on PyTorch, including SCNN, RESA, UFLD, LaneATT, CondLane, etc.
Code for the paper entitled "PolyLaneNet: Lane Estimation via Deep Polynomial Regression" (ICPR 2020)
Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.
Pytorch implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding"
Built a real-time lane departure warning system with a monocular camera, using OpenCV.
OpenCV implementation of lane and vehicle tracking
Vehicle Detection with Convolutional Neural Network
This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation
Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer (IV 2020)
Lane and obstacle detection for active assistance during driving. Uses windowed sweep for lane detection. Combination of object tracking and YOLO for obstacles. Determines lane change, relative velocity and time to collision
Inter-Region Affinity Distillation for Road Marking Segmentation (CVPR 2020)
Deep learning based lane/freespace detector embedded in ROS node (built for UC3M LSI)
The deep semantic segmentation network for lane segmentation.
An advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding.
Ros package for basic autonomous lane tracking and object detection
Lane detection and classification in an end-to-end Deep Learning fashion
Gathers signal processing, computer vision, machine learning and deep learning for self-driving car engines.
Using OpenCV to detect Lane lines on Roads
Use segmentation networks to recognize lane lines and vehicles. Infer position and curvature of lane lines relative to self.
This is the final project for the Geospatial Vision and Visualization class at Northwestern University. The goal of the project is detecting the lane marking for a small LIDAR point cloud. Therefore, we cannot use a Deep Learning algorithm that learns to identify the lane markings by looking at a vast amount of data. Instead we will need to build a system that is able to identify the marking just by looking at the intensity value within the point cloud.
Lane detection model for mobile device via MNN project