Research on tire crack detection using image deep learning method
Problem
Tire defects such as cracks and debris are hard to detect manually, creating safety risks and labor costs for drivers and manufacturers.
Why it matters
Undetected tire defects compromise vehicle safety, performance, and efficiency, and manual inspection is costly and slow.
What the AI did
A deep learning method based on an improved ShuffleNet was designed to detect tire defects from images, compared against GoogLeNet, ShuffleNet, VGGNet, and ResNet.
Results
94.7% detection rate for tire debris defects on the authors' tire database.
Impact
The abstract claims effective detection reducing labor costs and inspection time, validated on the authors' own database.
Record
| Venue | Scientific Reports |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Shih-Lin Lin |
| Citations | 35 (OpenAlex, at import time) |
| References | 49 |
| Model or method | Improved ShuffleNet |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41598-023-35227-z |
| Open access | gold |
| Keywords | tire crack detection, deep learning, ShuffleNet, image classification, defect inspection |
Importance rating
real but narrower advance
Tire defects affect driver safety and manufacturer costs per abstract, but no scale (vehicles, market, injury figures) is quantified; a real but narrower inspection advance.
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