Road Damage Detection at Scale: YOLOv5 Ensemble for Real-Time, Low-Cost Road Infrastructure Monitoring
Built and benchmarked a smartphone-first road-damage detector using YOLOv5 (with ensembling + test-time augmentation) and Faster R-CNN on the Global Road Damage Detection dataset. Achieved a top-5 leaderboard result (F1 0.68) across 121 teams while meeting a 0.5s/image inference target—enabling practical, low-cost deployment from dashboard-mounted phones. Includes a mapping concept (GPS → segment scores) to guide maintenance prioritization for DOTs and municipalities.
Sep 7, 2025
A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management
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Feb 7, 2022