AI Article Synopsis
- A lightweight YOLOv5-Lite-s neural network was implemented on embedded devices to automate the recognition and positioning of traffic cones during highway maintenance operations.
- The system utilized a shuffle Net backbone for efficient feature extraction and modified convolutional layers to enhance speed while preserving model accuracy.
- Experimental results indicated that the system could recognize cones with about 89% accuracy at 9 frames per second, successfully supporting cone operations at a rate of 30 cones per minute while maintaining stability in various environmental conditions.
Article Abstract
To improve the automation level of highway maintenance operations, the lightweight YOLOv5-Lite-s neural network was deployed in embedded devices to assist an automatic traffic cone retractor in completing recognition and positioning operations. The system used the lightweight shuffle Net network as a backbone for feature extraction, replaced convolutional layers with focus modules to reduce computational complexity, and reduced the use of the C3 layer to increase network speed, thereby meeting the speed and accuracy requirements of traffic cone placement and retraction operations while maintaining acceptable model inference accuracy. The experimental results show that the network could maintain recognition accuracy and speed values of around 89% and 9 fps under different working conditions such as varying distances, lighting conditions, and occlusions, meeting the technical requirements for deploying and retrieving cones at a speed of 30 cones per minute when the operating vehicle's speed was 20 km/h. The automatic traffic cone placement and retraction system operated accurately and stably, achieving the application of machine vision in traffic cone retraction operations.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598825 | PMC |
| http://dx.doi.org/10.3390/s24227190 | DOI Listing |
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