Improved lightweight YOLOv5 based on ShuffleNet and its application on traffic signs detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an efficient, lightweight traffic sign detection framework for intelligent driving. The improved YOLOv5 model enhances detection speed by 20.8% while maintaining accuracy, making it suitable for embedded systems.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Autonomous Driving Systems
Background
- Traffic sign detection is crucial for intelligent driving systems.
- Existing methods face challenges with complex environments and varying object scales.
Purpose Of The Study
- To develop an improved, lightweight traffic sign detection framework based on YOLOv5.
- To enhance detection accuracy and speed, particularly for small and inconspicuous traffic signs.
Main Methods
- Replaced YOLOv5 backbone with ShuffleNet v2 for reduced complexity.
- Integrated CA attention mechanism to improve saliency of traffic signs.
- Designed BCS-FPN for multi-scale feature fusion to enhance small object representation.
Main Results
- The proposed framework achieved equivalent mAP to YOLOv5s with a 20.8% speed improvement.
- Demonstrated superior performance on the TT-100K dataset.
- Showcased effectiveness on embedded devices with limited computing power.
Conclusions
- The enhanced YOLOv5 framework offers a computationally efficient and effective solution for traffic sign detection.
- The model's performance on embedded systems indicates its practical applicability in real-world intelligent driving scenarios.
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