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MSCD-YOLO: A Lightweight Dense Pedestrian Detection Model with Finer-Grained Feature Information Interaction
Qiang Liu1, Zhongmin Li1, Lei Zhang1
1School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.
Sensors (Basel, Switzerland)
|January 25, 2025
Summary
MSCD-YOLO is a new lightweight model for dense pedestrian detection, improving accuracy and robustness with fewer parameters. This approach enhances real-time surveillance and urban traffic systems.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Dense pedestrian detection is vital for applications like surveillance and traffic management.
- Existing methods struggle with accuracy, miss rates, large model sizes, and robustness.
Purpose of the Study:
- To propose MSCD-YOLO, a lightweight dense pedestrian detection model.
- To enhance detection accuracy, performance, and robustness while minimizing parameters.
Main Methods:
- Utilized MobileViT as a lightweight backbone for efficient local and global feature extraction.
- Designed the SCNeck neck network for information-preserving feature fusion.
- Employed the DEHead detection head for multi-scale feature fusion.
Main Results:
- MSCD-YOLO demonstrated improved mAP@0.5 and mAP@0.5:0.95 on Crowdhuman and Widerperson datasets compared to YOLOv8n.
- Achieved significant gains in detection accuracy and efficiency with reduced model complexity.
Conclusions:
- MSCD-YOLO offers a superior solution for dense pedestrian detection challenges.
- The model provides high accuracy and efficiency with a reduced parameter count.
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