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Related Experiment Video

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

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Published on: December 15, 2023

Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5.

Mingwu Wang¹, Dan Qu², Zedong Wu¹

¹Department of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

A new lightweight neural network (YOLOv5-Lite-s) enhances highway maintenance automation by enabling automatic traffic cone recognition and positioning. This system achieves high accuracy and speed for efficient cone deployment and retraction operations.

Keywords:

automatic traffic cone retractor deep learning network deployment road maintenance target detection

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Highway maintenance operations require efficient and automated traffic cone deployment and retraction.
Existing systems may lack the speed and accuracy needed for real-time operations.
Embedded systems offer potential for on-site processing but require optimized models.

Purpose of the Study:

To develop and deploy a lightweight neural network for automated traffic cone recognition and positioning.
To improve the automation level of highway maintenance operations using embedded devices.
To meet the speed and accuracy requirements for traffic cone placement and retraction.

Main Methods:

Utilized the lightweight YOLOv5-Lite-s neural network with a ShuffleNet backbone for feature extraction.
Reduced computational complexity by replacing convolutional layers with focus modules and minimizing C3 layer usage.
Deployed the optimized network on embedded devices for real-time traffic cone recognition and positioning.

Main Results:

The YOLOv5-Lite-s network achieved approximately 89% recognition accuracy and 9 frames per second (fps) under varied conditions (distance, lighting, occlusion).
The system met technical requirements for deploying/retrieving 30 cones per minute at a vehicle speed of 20 km/h.
Demonstrated accurate and stable operation of the automatic traffic cone placement and retraction system.

Conclusions:

The lightweight YOLOv5-Lite-s network effectively enables machine vision applications in traffic cone retraction operations.
The developed system enhances highway maintenance automation with acceptable model inference accuracy and speed.
The optimized neural network is suitable for deployment on embedded devices for real-time traffic management tasks.