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<p>Welding defects in steel structures are difficult to control, leading to fatigue cracking of the weld. This study develops an improved AlexNet model for welding defect detection and classification of steel structures. An experimental study on welding defect detection in steel plates was conducted. An optimal image preprocessing method for steel welds was proposed. And the effectiveness of this method is proved by comparative experiments. Steel structural components were produced including six types of weld defects. Based on the characteristics of weld defects in steel structures, the framework of the network model was optimized and an improved AlexNet network model was constructed. The numerical results show that the convergence accuracy of the improved AlexNet model reaches 98%, while the respective convergence accuracies of conventional AlexNet and GoogLeNet are 90.0% and 94.0%, respectively. Moreover, the improved AlexNet model has better performance in terms of training speed and prediction accuracy.</p>

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Welding Defect Classification for Steel Structures Using AlexNet

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Naiwei Lu,
  • Longkai Li,
  • Honghao Wang,
  • Kai Wang

摘要

Welding defects in steel structures are difficult to control, leading to fatigue cracking of the weld. This study develops an improved AlexNet model for welding defect detection and classification of steel structures. An experimental study on welding defect detection in steel plates was conducted. An optimal image preprocessing method for steel welds was proposed. And the effectiveness of this method is proved by comparative experiments. Steel structural components were produced including six types of weld defects. Based on the characteristics of weld defects in steel structures, the framework of the network model was optimized and an improved AlexNet network model was constructed. The numerical results show that the convergence accuracy of the improved AlexNet model reaches 98%, while the respective convergence accuracies of conventional AlexNet and GoogLeNet are 90.0% and 94.0%, respectively. Moreover, the improved AlexNet model has better performance in terms of training speed and prediction accuracy.