In the production process of steel strips, the detection of surface defects is very important. However, traditional methods of defect detection bring problems of low detection accuracy and dependence on subjective judgment. In this study, the surface defects of steel strips are detected by a classic convolutional neural network method that is improved by the use of a transfer learning model. This model has the advantages of shorter training time, faster convergence, and more accurate weight parameters. The transfer learning model obtained through experiments secures better results in defect detection than the classic convolutional neural network method, as its accuracy of training and testing has reached about 98%. Finally, a model based on a full convolutional neural network (FCN) is proposed for segmenting the defective areas of steel strips.

中文翻译：

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基于迁移学习模型的热轧带钢表面检测

在钢带生产过程中，表面缺陷的检测非常重要。然而，传统的缺陷检测方法存在检测精度低和依赖主观判断的问题。在本研究中，通过使用迁移学习模型改进的经典卷积神经网络方法检测钢带表面缺陷。该模型具有训练时间更短、收敛速度更快、权重参数更准确等优点。通过实验得到的迁移学习模型在缺陷检测方面比经典的卷积神经网络方法有更好的结果，训练和测试的准确率都达到了98%左右。最后，提出了一种基于全卷积神经网络（FCN）的模型来分割钢带缺陷区域。