Enhancing Road Safety on US Highways: Leveraging Advanced Computer Vision for Automated Guardrail Damage Detection and Evaluation

Abstract

Roadside incidents are a leading cause of driver fatalities in the United States, with a significant number involving collisions with barriers, such as guardrails. Guardrails are essential safety barriers designed to maintain vehicle trajectories and shield against roadside hazards. The functionality of guardrails heavily relies on their structural integrity, and damaged guardrails can pose serious dangers to road users. Traditional inspection methods are labor-intensive, time-consuming, and prone to human error, lacking periodic monitoring crucial for timely maintenance. Although advancements in computer vision have enabled automated infrastructure inspections, research dedicated specifically to the inspection of guardrails remains scarce. Existing automated solutions do not fully address the challenges of accurately identifying and assessing guardrail damage under varying lighting and weather conditions and the computational demands of real-time processing. This study addresses these challenges by introducing a novel framework utilizing advanced computer vision techniques, such as YOLOv8 models and the Deep OC–SORT tracker, integrated with camera and GPS systems mounted on a vehicle. This system automates the detection, localization, and severity assessment of guardrail damage, enhancing inspection accuracy and efficiency, enabling faster maintenance responses, and ultimately contributing to safer road conditions.