## 2 Related Studies  
The number of studies using machine learning to identify surface defects of steel has increased during the past few years. [PERSON] et al. [9] performed automatic surface fault detection of rolled steel using artificial neural networks with image processing. The classification of certain defects, including clamps, holes, and welding, was detected by the image analysis Hough Transform method, while other complicated defects, such as exfoliation, oxidation, and waveform, were detected by applying Self-Organizing Maps and Principal Component Analysis to extract features. The system achieved an overall accuracy of 87% after managing real-world datasets. [PERSON] et al. [10] designed a feature fusion-based method to improve steel surface defect detection in their proposed model. In this method, a multiscale feature extraction (MSFE) strategy is adopted from a YOLO-based model. With the MSFE algorithm, features with different scales are extracted from multidimensional kernels of different convolution layers. An efficient feature fusion technique is then used to maximize feature discriminability. This model, developed on the publicly available NEU-DET dataset, achieved an optimum accuracy of 73.08%. [PERSON] and [PERSON] [11] developed a graph neural network-based method for detecting steel surface defects. They also used a novel attention mechanism called HDmA in this approach. This strategy was also successful in detecting defects in different fields of view. This method was tested on NEU-DET and GC-10 datasets and achieved 79.04% and 66.93% accuracy, respectively. [PERSON] et al. [12] presented a model based on YOLO v5 for detecting defects on steel surfaces. A multi-feature fusion technique, Res2 Attention blocks, was used to improve the performance of the model. Model performance was tested on the NEU-DET and GC10-DET datasets. The classification accuracies were 78.5% and 67.3%, respectively. A vision-based automatic detection method with three-section defect detection, region extraction, and industrial liquid quantification was proposed by [PERSON] et al. [13]. They discovered that industrial liquids were measured with an accuracy rate of 90%. The accuracy rate of the recognition of cracks and scratches was obtained as 91% or more. By creating a dataset with six defects: scars, scratches, inclusions, burrs, seams, and iron scales. [PERSON] et al. [14] explored the surface defect recognition of steel strips by converting You Only Look Once (YOLO) completely into convolutional layers. The rates of detection, recall, and mAP were 99%, 95.86%, and 97.55%, respectively. [PERSON] et al. [15] applied the SqueezeNet model by pre-training on the dataset of ImageNet for the classification of six different defects, which are rolled-in scales, inclusions, patches, scratches, crazing, and pitted surfaces. They came to the conclusion that the learned features significantly outperformed the hand-crafted ones for datasets that were heterogeneous and unseen. By using the strong time-sequenced properties of defects, [PERSON] et al. [16] studied steel defect detection periodically by using a convolutional neural network (CNN) and a long short-term memory (LSTM) to detect roll mark defects. After extracting the defect features of samples with CNN, vectors were added to the long- and short-term memory to detect defects. As a result, the proposed method reached 81.9% of the performance of defect detection and outperformed the CNN approach. The performance of the system was raised to a rate of 6.2% by enhancing the attention mechanism. [PERSON] et al. [17] utilized the dataset of NEU-CLS to improve the concurrent convolutional neural network (ConCNN) with light weight by using different scales of samples. The method's accuracy performance was found to be 98.89% with a duration of 5.58 ms. The classification pre-training of surface defects was conducted using VGG19 by [PERSON] et al. [18]. Feature extraction from the various levels of the defect weight model was performed using VGG19.