Papers with Code 아카이브

Road Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 1
평가표: 3
보존된 평가 기록: 9

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/YXu556/RoadExtraction

Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation

https://github.com/ahmadelsallab/MultiCheXNet

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/code-implementation1/Code1/tree/main/dlinknet

https://github.com/divyakraman/SAfE-Self-Attention-Based-Unsupervised-Road-Safety-Classification-in-Hazardous-Environments

SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments

https://github.com/divyakraman/SS-SFDA-Self-Supervised-Source-Free-Domain-Adaptation-for-Road-Segmentation-in-Hazardous-Environme

SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments

https://github.com/twigsWHy/dlinknet

https://github.com/yangyucheng000/dlinknet

https://github.com/ziyuan400/video_segmentation

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.