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An improved lightweight algorithm for traffic sign detection.

Jia Xu¹, Yingjie Du², Yuwei Yi¹

¹School of Automobile and Transportation, Tianjin University of Technology and Education, 300222, Tianjin, China.

Scientific Reports

|September 29, 2025

Summary

This summary is machine-generated.

This study introduces YOLO-SAL, a lightweight model for efficient traffic sign detection. It enhances accuracy and reduces computational load, crucial for autonomous driving systems.

Keywords:

Autonomous vehicles Lightweight algorithm Object detection and recognition YOLO-SAL (an improved algorithm)YOLOv8n

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Area of Science:

Computer Vision
Artificial Intelligence
Road Safety Technology

Background:

Accurate traffic sign detection is vital for road safety and autonomous driving.
Existing methods struggle with complex backgrounds, varying sign sizes, and high computational costs.

Purpose of the Study:

To develop a lightweight and efficient model for traffic sign detection.
To improve the accuracy and reduce the computational demands of traffic sign recognition systems.

Main Methods:

Introduced the SCC2f design, optimizing convolutional blocks for reduced parameters and computation.
Enhanced the Adaptive Feature Pyramid Network (AFPN) for improved multi-scale feature interaction.
Integrated Long-Sequence Knowledge Attention (LSKA) for focused information processing.

Main Results:

YOLO-SAL achieved a 4.9% increase in mean Average Precision (mAP) compared to YOLOv8n.
Reduced model parameters by 13.3% and computational load (FLOPs) by 8.6%.
Demonstrated superior performance in detecting traffic signs across various sizes and conditions.

Conclusions:

YOLO-SAL offers a significant advancement in lightweight traffic sign detection.
The model effectively balances accuracy and efficiency for real-world autonomous driving applications.
This research contributes to the development of safer and more capable autonomous vehicles.