# A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects
## 1 Introduction  
As a crucial product in the steel industry, flat steel has a wide application in industries such as shipbuilding, aviation, manufacturing, and automobiles. It constitutes more than 65% of the steel production capacity. Therefore, any quality issues with steel can result in significant losses for manufacturers and consumers alike. Notably, the biggest threat to thin and wide flat steel is surface defects [1]. Due to low efficiency and high subjectivity, traditional manual detection methods are being steadily replaced by various detection algorithms. However, poor robustness and real-time performance make it difficult for some defect detection algorithms, such as edge detection [2], wavelet transform [3], genetic algorithm [4], support vector machine [5], and so on, to meet the requirements of online defect detection. As a result, the intelligent development of steel defect detection is greatly limited. With the ongoing development of deep learning technology, an increasing number of companies are introducing intelligent recognition technology to replace traditional defect detection algorithms [6]. The use of convolutional neural networks (CNNs) [7, 8] in steel surface defect recognition significantly improves its accuracy. [PERSON] et al. [9] introduced a multi-level feature fusion network that achieved 92% accuracy by fusing picture characteristics obtained at each stage. [PERSON] et al. [10] proposed the DF-ResNet50 network model, which applies a characteristic neural network and decentralized attention network to enhance steel defect detection performance. However, achieving relatively reliable accuracy with CNNs requires a large number of training samples, which makes their use difficult for industrial applications. The introduction of TL (transfer learning) has greatly alleviated the problem of model underfitting in small sample settings. By utilizing pre-trained models trained on large-scale datasets [11], TL applies feature extraction capabilities learned from a vast number of samples to other fields [12]. Regarding the application of TL, [PERSON] et al. (2013) used a TL model to improve steel defect detection and experimentally verified that this approach outperforms traditional CNNs in terms of both training and testing accuracy, achieving an accuracy of approximately 98%. Based on these findings, [PERSON] et al. (2014) studied the effectiveness of combining two CNN architectures (MobileNet-V2 and Xception) with TL, proposing a method that not only achieves high recognition accuracy but also quickly executes. [PERSON] et al. (2015) improved the VGG19 network based on fast image preprocessing algorithms and TL, resulting in a recognition accuracy of 97.8% even under conditions of few samples and imbalanced datasets. It can be seen from the above that using TL-based CNN models can enhance defect detection performance. However, due to their numerous parameters, CNNs can display unpredictable behavior ([PERSON] et al., 2016). For example, even if CNN has superior performance and good generalization on object recognition tasks, [PERSON] et al. (2017) found a method to arbitrarily alter the network's prediction results by making imperceptible changes to input images. These modified inputs are called \"adversarial examples\". [PERSON] et al. (2018) showed a method to generate images that are similar to white noise, and which are very difficult for humans to recognize. However, CNNs can identify them as objects with very high confidence (99.99%). The above research indicates that while CNNs perform well on many tasks, their underlying mechanisms may significantly differ from those of humans and are not yet fully comprehended. There is still a lack of explanatory analysis in steel surface defect recognition applications. This paper attempts to apply an interpretable algorithm to perform explanatory analysis on pre-trained models in order to reveal why the model has good recognition performance and to explore its focus on different types of defects.