Paper Title
Efficient image Segmentation for Road Transport System

Abstract
This paper presents a novel approach for efficient image segmentation in the context of road transport systems using the YOLOv5 algorithm. The objective is to accurately detect and segment vehicles in traffic scenes for traffic monitoring and management. The proposed method is tested against state-of-the-art segmentation methods on a dataset of traffic images. According to the findings, the YOLOv5 algorithm achieves highly accurate and high throughput times, making it suitable for actual traffic monitoring applications. The paper concludes with a discussion of the proposed approach's strengths and weaknesses, as well as future research directions. Keywords - Image Segmentation, Road Transport System, Yolov5 Algorithm, Traffic Monitoring, Object Detection Computer Vision, Convolutional Neural Networks, Deep Learning, Semantic Segmentation.