AI-Based Road Safety Audit Automated Detection and Deterioration Assessment of Highway Safety Elements
Year: 2025
Type: article
Abstract: Ensuring road safety requires continuous inspection and maintenance of critical infrastructure such as lane markings, signboards, and barriers. Traditional manual inspections are time-consuming, expensive, and prone to inconsistencies, leading to delays in identifying deteriorated safety products and increasing accident risks. This study presents an AI-powered solution that automates road safety audits using computer vision[1]. An object detection model identifies road safety elements, and a segmentation model evaluates their deterioration levels by classifying defects such as rust, fading, or structural damage. The deterioration percentage determines the classification: Good (≤30%) – No immediate action required; Moderate (31–70%) – Requires maintenance within a reasonable timeframe; Bad (>70%) – Requires urgent replacement or repair. The implemented system achieves a minimum accuracy rate of 87.5% in detecting and classifying road safety elements, contributing to a 40% reduction in inspection costs and enabling proactive maintenance scheduling. By automating road safety audits, this system enhances detection accuracy, reduces manual inspection costs, and enables scalable, real-time monitoring of highways[11].
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409379706