Mengyang Li
ABSTRACT
The recognition of tire sidewall text refers to the technology that automatically detects and extracts textual information on tire sidewalls in dark background. This article explores the importance of tire sidewall text recognition and proposes a lightweight network for detecting key imprint text regions based on You Only Look Once v5 (YOLOv5). To enhance detection accuracy whilst minimizing parameter and computational complexity, this article replaces the backbone network of YOLOv5 with the EMO network and introduces the C3Faster module proposed in this article to replace the original C3 module in YOLOv5's Neck network, because convolution operations in the C3 module introduce a lot of computation. This accelerates inference while mitigating parameters and computational complexity. Additionally, the number of detection heads is changed to provide more suitable scale outputs on the dataset. Experimental results demonstrate that the refined algorithm yields a 12.1% enhancement in accuracy when compared to YOLOv5s.
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Index Terms
- The Tire Sidewall Key Information Region Detection Algorithm based on the Improvement of YOLOv5
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