Summary of Optimizing Helmet Detection with Hybrid Yolo Pipelines: a Detailed Analysis, by Vaikunth M et al.
Optimizing Helmet Detection with Hybrid YOLO Pipelines: A Detailed Analysis
by Vaikunth M, Dejey D, Vishaal C, Balamurali S
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper compares recent YOLO models for helmet detection in public road traffic dynamics. The goal is to evaluate reliability and computational load of YOLOv8, YOLOv9, and YOLOv11 models. A new hybridized YOLO model (h-YOLO) is proposed and tested against individual models using recall, precision, and mAP benchmarks. Training and testing times are also recorded to analyze real-time detection capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at different ways to detect helmets in traffic. It compares some popular computer vision models called YOLO to see which one works best. They even came up with a new way of combining these models, which they call h-YOLO. This new model is tested against the others to see how well it does. The results show that this new model is better for finding helmets than the individual models. |
Keywords
» Artificial intelligence » Precision » Recall » Yolo
