Advancements in Road Safety: A Comprehensive Review of YOLOv8 Models for Speed Breaker Detection and Road Segmentation

Abstract

Abstract: This literature review critically examines recent strides in road safety, centering on the integration of YOLOv8 models for speed breaker detection and road segmentation. The paper illuminates the project's triumphs, underscoring the multifaceted applications of YOLOv8 in intelligent transportation systems, advanced driver assistance systems (ADAS), and the evolving landscape of autonomous vehicles. The exploration encompasses an insightful overview of traditional road safety systems, elucidating their limitations and the imperative for advanced technologies like YOLOv8. It delves into the rich tapestry of research integrating YOLOv8 models into broader road safety systems, elucidating their benefits and discerning potential challenges. The assessment further navigates through performance evaluations, featuring an array of studies analyzing YOLOv8's efficacy in road safety contexts. It scrutinizes case studies and real-world applications where YOLOv8-based integrated road safety systems have been successfully implemented, shedding light on both triumphs and tribulations. The discourse expands to future trends and research directions, foreseeing the trajectory of integrated road safety systems and their synergy with evolving computer vision technologies. Altogether, this literature review offers a panoramic understanding of YOLOv8's role in reshaping road safety paradigms, simultaneously outlining challenges and heralding future possibilities in this dynamic domain.