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<p>With the advancement of computer technology, dynamic detection of damage to tire in-service has become feasible. However, current methods often struggle with accuracy limitations when confronted with specific working conditions and external factors. To address this challenge, we propose an intelligent recognition system for in-service tire damage driven by Strong Combination Augmentation and Contrast Fusion. The system uses a key feature learning enhancement method to address the problem. It uses the Hough transform and the Perceptual Hash algorithm to perform secondary feature comparison, enabling tire region detection even in low-resolution and high-interference scenarios. To effectively eliminate interference caused by wear, stains, and similar factors, we also introduce an efficient damage detection network called CA-EffNet. This network employs a strategic approach that combines various augmentation techniques and parameters with contrast fusion within a supervised learning framework. By integrating these elements, CA-EffNet expands the feature exploration space and effectively captures key damage features. The results show that the system efficiently achieves real-time detection within just 0.7&#xa0;s at speeds of up to 15&#xa0;km/h, meeting strict detection requirements. These results highlight the potential of the system to significantly advance the field of tire damage detection and ultimately contribute to safer roads.</p>

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Intelligent recognition system of in-service tire damage driven by strong combination augmentation and contrast fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月4日

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  • Dagang Shen,
  • Jinfeng Cao,
  • Peng Liu,
  • Jihong Guo

摘要

With the advancement of computer technology, dynamic detection of damage to tire in-service has become feasible. However, current methods often struggle with accuracy limitations when confronted with specific working conditions and external factors. To address this challenge, we propose an intelligent recognition system for in-service tire damage driven by Strong Combination Augmentation and Contrast Fusion. The system uses a key feature learning enhancement method to address the problem. It uses the Hough transform and the Perceptual Hash algorithm to perform secondary feature comparison, enabling tire region detection even in low-resolution and high-interference scenarios. To effectively eliminate interference caused by wear, stains, and similar factors, we also introduce an efficient damage detection network called CA-EffNet. This network employs a strategic approach that combines various augmentation techniques and parameters with contrast fusion within a supervised learning framework. By integrating these elements, CA-EffNet expands the feature exploration space and effectively captures key damage features. The results show that the system efficiently achieves real-time detection within just 0.7 s at speeds of up to 15 km/h, meeting strict detection requirements. These results highlight the potential of the system to significantly advance the field of tire damage detection and ultimately contribute to safer roads.