Scholarlink

<p>Tire X-ray defect detection is vital for ensuring product safety and quality. However, manual inspection methods are prone to errors and inefficiencies. This study aims to explore a new tire defect detection model algorithm YOLO-GUAC with deep learning techniques to address these challenges. To address these challenges, we introduce and adapt the Global Attention Mechanism to enhance feature extraction, particularly for small defects against complex backgrounds. We further incorporate a Self-Calibrated Convolution module into the backbone and neck networks to optimize spatial and channel information flow while reducing redundancy. Additionally, an Auxiliary Detection Head is integrated to provide intermediate supervision, minimizing information loss during training. The experimental results show that the mAP@0.5 accuracy of YOLO-GUAC reaches 95% and the accuracy of mAP@0.5–0.95 is improved by 3.7%. Our model can effectively detect tire defects better than current mainstream methods, thus highlighting its potential in practical applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Global Attention-Guided Self-Calibrating Tire Defect Detection Using Deep Learning

  • 期刊论文

  • 发表日期：

    2026年3月14日

查看全文
  • Yue Chen,
  • Nian Wang,
  • Weiang Li,
  • Liyang Zhang,
  • Mengxin Chen,
  • Jinghong Tian

摘要

Tire X-ray defect detection is vital for ensuring product safety and quality. However, manual inspection methods are prone to errors and inefficiencies. This study aims to explore a new tire defect detection model algorithm YOLO-GUAC with deep learning techniques to address these challenges. To address these challenges, we introduce and adapt the Global Attention Mechanism to enhance feature extraction, particularly for small defects against complex backgrounds. We further incorporate a Self-Calibrated Convolution module into the backbone and neck networks to optimize spatial and channel information flow while reducing redundancy. Additionally, an Auxiliary Detection Head is integrated to provide intermediate supervision, minimizing information loss during training. The experimental results show that the mAP@0.5 accuracy of YOLO-GUAC reaches 95% and the accuracy of mAP@0.5–0.95 is improved by 3.7%. Our model can effectively detect tire defects better than current mainstream methods, thus highlighting its potential in practical applications.