Summary of Yolov11 For Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems, by Mujadded Al Rabbani Alif
YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems
by Mujadded Al Rabbani Alif
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel deep learning model, YOLO11, is introduced as a significant advancement in vehicle detection tasks. Building upon its predecessors, YOLO11 improves speed, accuracy, and robustness in complex environments. The model is evaluated using precision, recall, F1 score, mean average precision (mAP), and inference time on a comprehensive dataset containing various vehicles. Results show that YOLO11 outperforms previous versions in detecting smaller and occluded vehicles while maintaining competitive inference times, making it suitable for real-time applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new deep learning model called YOLO11, which is designed to detect vehicles more accurately and quickly than before. It works by improving the speed and accuracy of its predecessors, making it better at detecting smaller and harder-to-spot vehicles while still being fast enough for real-time use. The researchers tested YOLO11 on a big dataset with many different types of vehicles and found that it did better than earlier versions in detecting small and occluded vehicles. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Inference » Mean average precision » Precision » Recall
