Papers with Code 아카이브
Lane Detection
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Lane Detection is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.
Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.
<span style="color:grey; opacity: 0.6">( Image credit: End-to-end Lane Detection )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sephirex-x/adnet
공식 코드https://github.com/shqww/polarrcnn
공식 코드https://github.com/tusen-ai/anchor3dlane
공식 코드Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection
https://github.com/vincentqqb/priorlane
공식 코드PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer
https://github.com/voldemortX/pytorch-auto-drive
공식 코드https://github.com/weiqingq/CLRKDNet
공식 코드CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
https://github.com/wolfwjs/ganet
공식 코드A Keypoint-based Global Association Network for Lane Detection
https://github.com/wudongming97/topomlp
공식 코드TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
https://github.com/wuyuej/dhpm
공식 코드https://github.com/wvangansbeke/LaneDetection_End2End
공식 코드End-to-end Lane Detection through Differentiable Least-Squares Fitting
https://github.com/ybarancan/stsu
공식 코드Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
https://github.com/yujun0-0/mma-net
공식 코드VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
https://github.com/yuliangguo/Pytorch_Generalized_3D_Lane_Detection
공식 코드Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection
https://github.com/yzichen/mmlanedet
공식 코드DALNet: A Rail Detection Network Based on Dynamic Anchor Line
https://github.com/zillur-av/LVLane
공식 코드LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions
https://github.com/zkyseu/O2SFormer
공식 코드https://github.com/2000aman/Self_Driving_Car_Project
https://github.com/AbangLZU/lanenet-lane-detection-py2
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/Abhaya1998/Self-Driving-Car
https://github.com/AeroZealous4/Self_Driving_Car_CNN
https://github.com/AhmadWaly/Endtoend-learning-for-self-driving-cars
https://github.com/ArasStar/comma_ai_speed_challenge2017_Tensorflow
https://github.com/Ashafix/NeuroMario
https://github.com/Ceachi/Project-Self-Driving-Car-Advanced-Lane-Lines
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
https://github.com/Dekki-Aero/Self-Driving-Car
https://github.com/Derteanoo/PINet_quantization
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/ElSchuer/e2e_lane_keeping
https://github.com/FabrizioPuzzo/CarND-Behavioral-Cloning-P4
https://github.com/FabrizioPuzzo/End-to-End-Learning
https://github.com/FloHofstetter/wcid-keras
Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving
https://github.com/GK4444/Self-Driving-Car
https://github.com/GT-RIPL/L2C
https://github.com/Garvit-32/Automating-Steering-Wheel-Self-Driving-Car
https://github.com/Garvit-32/Self_Driving_Car_Using_Keras
https://github.com/Git10Shikhar/Self-Driving-Cars
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.