DORIE: Dataset of Road Infrastructure Elements-A Benchmark of YOLO Architectures for Real-Time Patrol Vehicle Monitoring
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View abstract on PubMed
Summary
This summary is machine-generated.A new dataset, DORIE, enhances road safety by detailing infrastructure elements like guardrails and signs. This resource aids in developing intelligent systems for real-time monitoring and maintenance.
Area Of Science
- Computer Vision
- Road Safety Engineering
- Dataset Creation
Background
- Existing driving datasets lack comprehensive road infrastructure element representation.
- Critical safety elements like guardrails, bollards, and signs are underrepresented.
- This gap hinders the development of advanced road safety and maintenance systems.
Purpose Of The Study
- Introduce DORIE (Dataset of Road Infrastructure Elements), a high-resolution dataset for infrastructure monitoring.
- Provide a benchmark for real-time object detection of road infrastructure.
- Facilitate research in intelligent road safety and maintenance.
Main Methods
- Curated a novel dataset (DORIE) with 938 images and over 6800 annotations across ten categories.
- Evaluated YOLOv8, YOLOv11, and YOLOv12 object detection models on the DORIE dataset.
- Assessed performance across multiple scales and input resolutions, measuring mean Average Precision (mAP) and inference latency.
Main Results
- Larger YOLO models and higher resolutions improved mAP by up to 40%, especially for small objects like traffic signs and bollards.
- Inference latency varied from 5.7 to 245.2 ms, highlighting the accuracy-speed trade-off.
- Demonstrated the effectiveness of YOLO models for detecting road infrastructure elements.
Conclusions
- DORIE provides a valuable, reproducible resource for advancing road infrastructure monitoring research.
- The dataset and YOLO baselines support the development of intelligent road safety and maintenance systems.
- Optimizing detection models is crucial for real-time applications in road safety.
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