TrueFrame
Predicts optimal AV1 encoder settings in a single pass, removing the repeated trial encodes that drive up cloud costs and energy use.
Trinity College Dublin
Lead: Dr. Vibhoothi Vibhoothi
Sustainability & Environmental ManagementRoad AI
Automatically assesses road surface condition from imagery using computer vision, replacing slow manual inspection of defects such as potholes.
- Lead researcher
- Dympna O’Sullivan
- Institution
- Technological University Dublin
About the project
Road infrastructure maintenance presents major financial and safety challenges globally. In Ireland alone, over 60% of drivers report vehicle damage from potholes, while Transport Infrastructure Ireland allocated €35 million in 2023 to national road maintenance. The manual inspection of road conditions is time-consuming and resource-intensive, particularly in the defect identification and assessment stages. Road AI helped develop a holistic, intelligent software application for automatic road pavement condition assessment. Leveraging computer vision, image processing, machine learning and deep learning techniques
The key objectives of Road AI are:
1.Detecting and evaluating road defects from video data.
2.Classifying road segments using PSCI (Pavement Surface Condition Index) ratings.
3.Providing a user-friendly visual interface for engineers and surveyors.
Road AI established a deployable tool applicable to both Irish and US road contexts and a comprehensive application integrating deep learning object detection, PSCI classification algorithms, and high-definition LCMS/LiDAR data analysis.
Last updated: 9 September 2026
Project team
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