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Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

An Improved YOLO11n-Based Algorithm for Road Sign Detection.

Haifeng Fu¹, Xinlei Xiao¹, Yonghua Han¹

¹School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

This study introduces an improved YOLOv11n network for robust road sign detection in complex driving scenes. The enhanced model significantly boosts accuracy for multi-scale, distant, and low-resolution targets, improving intelligent vehicle perception.

Keywords:

MGA module improved SPPF module improved YOLO11n network neck road sign detection

Related Experiment Videos

Last Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Artificial Intelligence
Intelligent Transportation Systems

Background:

Road sign detection is crucial for intelligent vehicles but challenged by complex backgrounds, multi-scale targets, long distances, and low resolution.
Existing methods struggle to accurately detect road signs under adverse conditions, impacting the reliability of autonomous driving systems.

Purpose of the Study:

To develop an improved YOLOv11n network for enhanced road sign detection in complex driving environments.
To address challenges of multi-scale targets, long distances, and low resolution in road sign recognition.
To improve the accuracy and real-time performance of road sign detection systems for intelligent vehicles.

Main Methods:

Proposed a Multi-path Gated Aggregation (MGA) Module for multi-dimensional feature extraction to handle multi-scale and low-resolution targets.
Enhanced the network's Neck by incorporating high-resolution information from the Backbone to improve detection of small and blurry signs.
Introduced an enhanced Spatial Pyramid Pooling-Fast (SPPF) module with Group Convolution-Layer Normalization-SiLU for noise suppression and feature amplification.

Main Results:

The improved YOLOv11n model achieved an mAP@0.5 of 96.96%, a 1.42% increase over the original model.
Achieved mAP@0.5-0.95 of 83.94% and a Recall rate of 92.94%.
Maintained a high inference speed of 134 FPS, demonstrating real-time efficiency.

Conclusions:

The proposed modular enhancements effectively improve road sign detection accuracy and efficiency.
The method offers a superior balance between detection performance and real-time processing for complex scenarios.
Provides robust technical support for reliable intelligent vehicle perception systems.