Evaluating YOLO26s for Multi-Class Pavement Crack Detection: A Lightweight Approach for Sustainable Edge Deployment

Abstract

Highlights What are the main findings? YOLO26s, a lightweight deep learning model, achieves 89.0% mAP@0.5 for multi-class pavement crack detection (longitudinal, transverse, pothole, and alligator cracks) while reducing 14.3% parameters and 7.7% FLOPs compared to YOLOv8s, enabling efficient real-time edge deployment. Evaluated on a multinational dataset (RDD2022) spanning four countries (USA, Norway, Japan, China), YOLO26s demonstrates robust cross-domain generalization across diverse environmental conditions, road textures, and imaging perspectives. What are the implications of the main findings? The lightweight architecture supports sustainable infrastructure management by facilitating early, accurate crack detection on resource-constrained edge devices, reducing material waste, energy consumption, and carbon emissions from delayed or repeated road maintenance. The model’s real-time capability and computational efficiency make it scalable for large-scale road network monitoring, offering a practical AI-driven solution for municipalities and maintenance authorities in both developed and emerging regions. Abstract Maintaining durable road infrastructure is crucial for reducing resource consumption, minimizing repair costs, and supporting sustainable urban mobility. However, accurately detecting small and morphologically diverse pavement cracks remains challenging due to variations in lighting, road textures, and crack shapes across different geographic regions. YOLO (You Only Look Once) is one of the most widely adopted deep learning (DL) frameworks for object detection. Traditional inspection methods are labor-intensive and often inconsistent, while existing DL models can be computationally heavy or limited to single crack types, restricting real-time deployment and scalability. To address these challenges, this study presents YOLO26s, a lightweight DL model for multi-class pavement crack detection across diverse environmental and geographic conditions. Using a curated subset of 6972 annotated images from the Road Damage Dataset 2022, YOLO26s identifies four crack types: longitudinal, transverse, pothole, and alligator cracks. Compared to baseline models (YOLOv8s, YOLOv8n, YOLO26n), YOLO26s achieves higher detection accuracy (mAP@0.5 = 89.0%) while reducing computational complexity by 14.3% in parameters and 7.7% in FLOPs, enabling real-time deployment on edge devices. By facilitating early and accurate crack detection, the proposed approach supports proactive maintenance, extends pavement lifespan, and reduces material and energy usage, contributing to more sustainable road network management. These findings highlight the potential of efficient AI-driven inspection systems to enhance environmental and economic sustainability in civil infrastructure.