Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5
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View abstract on PubMed
Summary
This summary is machine-generated.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.
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.
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