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/rafa2000/Top-TensorFlow
https://github.com/rahul263-stack/Self-Driving-Car
https://github.com/rehamessameltagoury/Urban-Self-Driving-Car
https://github.com/sabadijou/clld_official
Contrastive Learning for Lane Detection via cross-similarity
https://github.com/sachinsharma9780/Master-Thesis-Free-Space-Detetction-for-Autonomous-Driving-using-Deep-Learning
Enhanced free space detection in multiple lanes based on single CNN with scene identification
https://github.com/sem6-nu/AI-PROJECT
https://github.com/shleecs/Copy-and-Paste-Networks-for-Deep-Video-Inpainting
https://github.com/shreyaspj20/Autonomous-Driving-Car-simulation-using-Pytorch
https://github.com/shunchan0677/deepware
https://github.com/snbhanja/SelfDrivingCarND_Term1_Project4_Behavioral_cloning
https://github.com/snehitvaddi/SelfDriving-Car_Deep-Learning
https://github.com/stesha2016/lanenet-enet-hnet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/theneuralbeing/behavioral-cloning
https://github.com/theshuv/Autonomous-vehicle-steering-angle-prediction
https://github.com/thoughtworks-hpc/PINetTensorrt
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/thouyang/self_driving_with_corner_cases
https://github.com/tsinghua-mars-lab/online-hd-map-construction-cvpr2023
https://github.com/ugururesin/CarND-Behavioral-Cloning
https://github.com/ugururesin/Udacity-CarND-Behavioral-Cloning
https://github.com/wenyuqing/panacea
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
https://github.com/wind754203900/SCNN-TF2
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/windwithforce/lane-detection
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/woosoojin/final
https://github.com/xiya888/lane_detect_convert
https://github.com/yanrucheng/PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/yanrucheng/PINet-demo
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/zhangjy2008327/lane-detection-with-double-convgrus
https://github.com/zhenye-na/e2e-learning-self-driving-cars
https://github.com/zkyntu/UnLanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/zkyseu/FlowLane
https://github.com/zkyseu/PPlanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.