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pro vyhledávání: '"Anhettigama, Damith"'
Autor:
Jayasinghe, Oshada, Hemachandra, Sahan, Anhettigama, Damith, Kariyawasam, Shenali, Wickremasinghe, Tharindu, Ekanayake, Chalani, Rodrigo, Ranga, Jayasekara, Peshala
Recent work done on traffic sign and traffic light detection focus on improving detection accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically with limited computational resources. In this work, we propose a sim
Externí odkaz:
http://arxiv.org/abs/2205.02421
Autor:
Jayasinghe, Oshada, Hemachandra, Sahan, Anhettigama, Damith, Kariyawasam, Shenali, Rodrigo, Ranga, Jayasekara, Peshala
In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailabi
Externí odkaz:
http://arxiv.org/abs/2110.11867
Autor:
Jayasinghe, Oshada, Anhettigama, Damith, Hemachandra, Sahan, Kariyawasam, Shenali, Rodrigo, Ranga, Jayasekara, Peshala
Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light
Externí odkaz:
http://arxiv.org/abs/2110.11779
Autor:
Jayasinghe, Oshada, Hemachandra, Sahan, Anhettigama, Damith, Kariyawasam, Shenali, Rodrigo, Ranga, Jayasekara, Peshala
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailabi