## 3 Literature Gap, Motivation, and Contributions  
To summarize the literature in general, in terms of deep learning, pre-trained models or lightweight CNN models have been used to classify defects on steel surfaces. Additionally, no frequency-time conversion algorithm has been used to increase the discrimination in the images. Looking at the literature, it is evident that many deep learning-based studies have been conducted to minimize defects in steel production. In studies focusing on the classification problem, the classification accuracy ranged between 80% and 90%. Especially in YOLO-based fault detection studies, the classification accuracy performance was between 65% and 75%. When these accuracy values are considered, it is obvious that the error performance of steel surfaces is still open to improvement. In a steel mill with a high production capacity, even a 2-3% increase in the defect detection performance will significantly improve the production quality. Therefore, there is a serious need for a deep learning-based application that detects steel surface defects with high performance.  
This study was carried out to distinguish and classify defects on steel surfaces, as a small improvement in defect detection in high-capacity steel production will provide a large number of defect-free steel products. The proposed approach is designed in four main stages. The first stage is the processing of raw images. The second stage is responsible for extracting features from the Parallel Attention-Residual CNN (PARC) model. The third stage involves feature selection with the Iterative Neighborhood Component Analysis (INCA) algorithm. The final stage includes classification with a powerful algorithm. The proposed approach has three important contributions, as outlined below.  
Attention and residual structures in the CNN model were added to the PARC model and trained in parallel. This increased the representation power of the features extracted from the PARC model. Therefore, the classification performance was improved. Although the parallel integration of attention and residual blocks has been studied in previous works, our proposed PARC architecture introduces a distinct combination specifically tailored for defect recognition in steel surface images. To validate its superiority, we conducted comparative experiments against baseline CNNs with only residual blocks, only attention modules, and sequential Attention-Residual designs. The results demonstrate that PARC achieves a consistently higher accuracy and robustness across both multi-class and binary classification tasks.
* Raw images were processed with a new approach. In this approach, 1D stack data are used instead of a signal to obtain spectrogram images. For 1D stack data, the gradient of the raw images was taken and then converted into sequential 1D stack data. The gradient operation highlighted the differences in pixel values in the image. As a result, the classification performance is improved with this pre-processing procedure. Existing methods all use pixel data. In this study, thanks to the transformation of the image into 1D stack data, it was possible to access the frequency information of images that form a pattern with each other, such as scratches and cracks. In this case, it increased the classification performance compared to raw images.