Comparative Analysis of Road Accident Detection using SVM and YOLOv8 in a Flask-based Web Application

Road accidents pose a serious global threat, causing significant loss of life and economic impact. This study presents a comparative analysis of two road accident detection methods—Support Vector Machine (SVM) optimized with Sequential Minimal Optimization (SMO), and the YOLOv8 deep learning algorithm—implemented within a Flask-based web application. While SVM focuses on image-based classification, YOLOv8 enables real-time object detection of various accident scenarios. The integrated system enhances detection accuracy, response speed, and overall road safety, offering promising advancements in intelligent traffic surveillance.