Demirel, ZeynepNasraldeen, Shvan TahirPehlivan, OykuShoman, SarmadAlbdairi, MustafaAlmusawi, Ali01. Çankaya Üniversitesi2025-11-062025-11-0620252673-7590https://doi.org/10.3390/futuretransp5030091https://hdl.handle.net/20.500.12416/15701Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms-specifically YOLOv8 and YOLOv11-for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform's uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments.eninfo:eu-repo/semantics/openAccessRoad Damage DetectionYOLOv8Deep LearningAI-Based DetectionPothole DetectionCrack DetectionArticle10.3390/futuretransp50300912-s2.0-105017488332