Computer vision research / USF Research
Road Asset Detection with YOLO
Computer vision pipeline for Boone County that detects and maps road infrastructure from stitched 360-degree roadway imagery.
Python / YOLOv8 / Roboflow / AWS / React / Computer Vision
Highlights
- Evaluated YOLO, LaneNet, and DINOv2 on a 7,650-image dataset.
- Trained YOLO models for lane markings, shoulders, barriers, streetlights, and other visually similar assets.
- Built a 3D visualization and validation tool, improving accuracy to 92% mAP and reducing manual labeling.
Context
This University of San Francisco research project supported Boone County roadway analysis using stitched 360-degree roadway imagery.
What I built
- A computer vision pipeline for lane markings, shoulders, barriers, streetlights, and other visually similar road assets.
- YOLO model training and evaluation alongside LaneNet and DINOv2 on a 7,650-image dataset.
- A 3D visualization and validation tool for inspecting detections and reducing manual labeling.
Result
The workflow improved accuracy to 92% mAP and reduced manual labeling work for road infrastructure detection.