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/klintan/pytorch-lanenet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/kodluyoruz-ML-bootcamp-1/BSSA
https://github.com/koolhussain/Self-Driving-Car
https://github.com/kun-g/Scraping-Github-trending
https://github.com/lekhraj-d/Self-Drive-Car-Implemention-Using-Keras
https://github.com/lh-wang/ACC
https://github.com/likui01/LDNet
LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor
https://github.com/littlemountainman/selfdrive
https://github.com/madhuridhangar/Self_Driving_Car
https://github.com/manish-vi/Autopilot-Tensoflow
https://github.com/mankadronit/SelfDrivingCar
https://github.com/manuTRON/self-driving-car
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/markus-k/ultrafast-lane-detection-tf2
https://github.com/marsauto/europilot
https://github.com/mbechtel2/DeepPicar-v2
https://github.com/mgei/sdcar
https://github.com/milsun/AI-Driver-CNN-DeepLearning-PyTorch
https://github.com/minghanz/LaneNet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/minoot7/Lane-Detection-Transfer-Learning
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/mrkatouei/Self-driving-smart-car-Autopilot-
https://github.com/ms5898/LaneNet-PyTorch
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/naman-mehta/predict_steering_angle
https://github.com/nandaswayam/Self-Driving-car
https://github.com/navoshta/behavioral-cloning
https://github.com/nerdimite/behavioral-cloning
https://github.com/nikkkkhil/lane-detection-using-lanenet
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/nishantml/Self-Driving-Car
https://github.com/notmariekondo/notmariekondo
https://github.com/nyoki-mtl/pytorch-discriminative-loss
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/pandamax/Lane-Detection-Based-PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/paulesta55/PINet
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/peterhong27/x-crop
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/pkoripalli/Self-Driving-Car
https://github.com/pratibhaareddy/selfdrivingcar
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.