Lane segmentation for advanced driver-assistance systems

dc.contributor.author	Temnyk, Maryana
dc.date.accessioned	2024-02-15T08:23:56Z
dc.date.available	2024-02-15T08:23:56Z
dc.date.issued	2020
dc.identifier.citation	Temnyk, Maryana. Lane segmentation for advanced driver-assistance systems / Temnyk, Maryana; Supervisor: Orest Varha; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2020. – 39 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4492
dc.language.iso	en	uk
dc.title	Lane segmentation for advanced driver-assistance systems	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Lane detection is an image analysis problem of detecting lane markings on the road. Recently it became one of the crucial parts of most Advanced diver-assistance systems. With the current progress in deep learning, plenty of new methods have been developed for lane detection. However, even novel powerful CNN architec- tures are struggling to give a satisfactory result along with acceptable performance. Recent studies show that developing a fast, robust and accurate model for real-time lane detection is a hard task due to external factors such as weather conditions, light- ing, traffic, complex marking shapes, thin or invisible lanes on rough and hilly roads, etc. The main focus of this thesis is to investigate, compare, and develop lane detec- tion methods that can be used in real-time ADAS applications.	uk