Papers with Code 아카이브
Road Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Road Segmentation is a pixel wise binary classification in order to extract underlying road network. Various Heuristic and data driven models are proposed. Continuity and robustness still remains one of the major challenges in the area.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection
공식 코드https://github.com/MarvinTeichmann/MultiNet
공식 코드MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/WYT8506/MultimodalCertification
공식 코드MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models
https://github.com/clovermini/skea_topo
공식 코드Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods
https://github.com/eerik98/lidar-camera-road-autolabeling
공식 코드Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter Conditions
https://github.com/erkanmilli/3mt-roadseg
공식 코드https://github.com/evocargo/lidar-annotation-is-all-you-need
공식 코드https://github.com/gaudetcj/DeepQuaternionNetworks
공식 코드https://github.com/hafeezkhan909/ALINA
공식 코드https://github.com/imrl/pathfinder
공식 코드Pathfinder for Low-altitude Aircraft with Binary Neural Network
https://github.com/isaaccorley/chesapeakersc
공식 코드https://github.com/lanmng/roadda
공식 코드https://github.com/linbaiwpi/RoadNet-RT
공식 코드RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
https://github.com/littleaich/deepglobe2018
공식 코드Semantic Binary Segmentation using Convolutional Networks without Decoders
https://github.com/morancyc/usnet
공식 코드Fast Road Segmentation via Uncertainty-aware Symmetric Network
https://github.com/nagellette/sentinel_traj_nn
공식 코드https://github.com/prassanna-ravishankar/slither
공식 코드Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts
https://github.com/pshams55/ASPN
공식 코드Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
https://github.com/savinay95n/PatchRefineNet
공식 코드https://github.com/tom-roddick/mono-semantic-maps
공식 코드Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks
https://github.com/walking-shadow/Official_Remote_Sensing_Mamba
공식 코드https://github.com/wgcban/spin_roadmapper
공식 코드https://github.com/wrld/PRoGAN
공식 코드PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
https://github.com/yeshwanth95/pix2poly
공식 코드https://github.com/yswang0522/NLLinkNet
공식 코드NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations
https://github.com/zhouhuan-hust/lfd-roadseg
공식 코드Exploiting Low-level Representations for Ultra-Fast Road Segmentation
https://github.com/2023-MindSpore-1/ms-code-25
https://github.com/2023-MindSpore-4/Code3/tree/main/dlinknet
https://github.com/ESA-PhiLab/PhilEO-MajorTOM
https://github.com/Evvvvvvvva/AutonomousDriving
https://github.com/MarvinTeichmann/KittiBox
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/MarvinTeichmann/KittiSeg
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/MrHongweiDu/Road_detection
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/ShirSherbet/Autonomous-Driving
https://github.com/XRONHH/text_1
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.