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<p>Heat exchangers are important equipment in the industrial sector, and the technology for cleaning the fouling on their densely packed tube ports is rapidly evolving towards automation, efficiency, and intelligence. To address the issues of missed detection, false detection, and repeated detection in existing visual algorithm models for tube port recognition, an improved YOLOv5 model integrating a dual attention mechanism, called DANet-YOLOv5 (Double Attention Net), is proposed. This model incorporates dual attention mechanism components into the backbone network, using second-order attention pooling, feature adaptive distribution, and efficient aggregation and propagation of global features. This allows the network to more comprehensively utilize both global and local information from the image, optimizing the sensitivity and accuracy of heat exchanger tube port image recognition. Experimental results demonstrate that this algorithm model outperforms existing small-object detection improvement algorithms, single attention mechanism improvement algorithms, and the mainstream YOLOv8 algorithm in key metrics such as error rate, recall rate, and mAP value, significantly enhancing the accuracy and stability of tube port detection and laying the foundation for the application of automated cleaning robots.</p>

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Heat exchanger pipe orifice recognition using an improved YOLOv5 model integrated with dual attention mechanisms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

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  • Ruijun Yang,
  • Yining Zhu,
  • Bin Xu,
  • Wenqi Dai,
  • Shoucheng Ji,
  • Dejian Meng

摘要

Heat exchangers are important equipment in the industrial sector, and the technology for cleaning the fouling on their densely packed tube ports is rapidly evolving towards automation, efficiency, and intelligence. To address the issues of missed detection, false detection, and repeated detection in existing visual algorithm models for tube port recognition, an improved YOLOv5 model integrating a dual attention mechanism, called DANet-YOLOv5 (Double Attention Net), is proposed. This model incorporates dual attention mechanism components into the backbone network, using second-order attention pooling, feature adaptive distribution, and efficient aggregation and propagation of global features. This allows the network to more comprehensively utilize both global and local information from the image, optimizing the sensitivity and accuracy of heat exchanger tube port image recognition. Experimental results demonstrate that this algorithm model outperforms existing small-object detection improvement algorithms, single attention mechanism improvement algorithms, and the mainstream YOLOv8 algorithm in key metrics such as error rate, recall rate, and mAP value, significantly enhancing the accuracy and stability of tube port detection and laying the foundation for the application of automated cleaning robots.