Authors: Md Nahid Sadik, Tahmim Hossain, Faisal Sayeed
Published on: April 11, 2024
Impact Score: 7.4
Arxiv code: Arxiv:2404.08081
Summary
- What is new: Use of advanced versions of YOLOv8 and RT-DETR models for real-time vehicle and pedestrian identification in complex urban environments.
- Why this is important: Existing traffic monitoring systems struggle with efficiently recognizing small objects and pedestrians in real-time, affecting public safety and traffic flow.
- What the research proposes: A deep-learning framework that utilizes YOLOv8 and RT-DETR models for improved recognition of cars and people in various environmental situations.
- Results: The YOLOv8 Large model outperformed others in pedestrian recognition with high precision and robustness, significantly enhancing traffic monitoring and safety.
Technical Details
Technological frameworks used: Advanced deep-learning
Models used: YOLOv8, RT-DETR
Data used: Dataset representing complex urban settings
Potential Impact
Autonomous driving companies, AI technology firms, video surveillance providers, traffic management systems
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