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/SeungyounShin/LaneNet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/SullyChen/Autopilot-TensorFlow
https://github.com/TimVerion/lanenet-lane-detection-master
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/Tsinghua-MARS-Lab/vectormapnet
https://github.com/Turoad/lanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/Vermankayak/Self-Driving-Car
https://github.com/Vipermdl/E2E-ERFNet
End-to-End Lane Marker Detection via Row-wise Classification
https://github.com/Will-J-Gale/GTA5-Self-Driving-Car
https://github.com/Wizaron/instance-segmentation-pytorch
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/XiangbingJi/Stanford-cs230-final-project
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/Zhenye-Na/self-driving-vehicles-sim-with-ml
https://github.com/abhiCoder123/Self-Driving-Car-Tensorflow
https://github.com/abhileshborode/Behavorial-Clonng-Self-driving-cars
https://github.com/acoMCMXCVI/End-to-end-Conditional-Imitation-Driving
https://github.com/aditbiswas1/P3-behavioral-cloning
https://github.com/akshaybahadur21/Autopilot
https://github.com/alicranck/instance-seg
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/amalbros/Self-Driving-Car-Simulation
https://github.com/ameyskulkarni/End-to-End-Learning-for-Steering-Angle-Prediction
https://github.com/anasbadawy/Steering-Prediction-CNN
https://github.com/aravindmanoharan/Self-Driving-Car-V1
https://github.com/aravindmanoharan/Self-Driving-Car-V2
https://github.com/arief25ramadhan/udacity-behavioral-cloning
https://github.com/as1067/PiCar
https://github.com/ashutosh1997/Self_Driving_Car
https://github.com/ashutoshkumar19/Self_Driving_Car
https://github.com/atlas604/behavioral-cloning
https://github.com/berkcomba/Self-Driving-Car-Keras
https://github.com/billpsomas/Lane_Detection_PyTorch
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/cciprianmihai/Self_Driving_Car_NanoDegree_P2_AdvancedLaneLines
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
https://github.com/chequanghuy/TriLiteNet
https://github.com/chingisooinar/End-to-End-Deep-Learning-Approach-for-Autonomous-Driving-DuckieTown
https://github.com/chuanhaoliu/CarND-Behavioral-Cloning
https://github.com/clarky104/carla_aebs
https://github.com/code-implementation1/Code2/tree/main/ADNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.