## 9 Discussion
[55] employed the SegNet model to reconstruct and segment images of steel surfaces, and subsequent classification using a CNN model with an end-to-end learning approach yielded a success rate of 89.60%. [PERSON] et al. [56] utilized a 14-layer CNN model featuring five convolutional layers, achieving an impressive classification accuracy of 99.05%. [PERSON] et al. [14] implemented a transfer learning system based on the ResNet model, attaining a classification accuracy of 99.00% with the ResNet CNN model. In another approach, [PERSON] et al. [15] leveraged the SqueezeNet framework to construct a lightweight CNN model, incorporating a blur operation for raw photo processing, resulting in a remarkable success percentage of 99.61%. The proposed approach outperformed the CNN, pre-trained CNN, R-CNN, transfer learning, and GAN models due to its novel and task-specific design. Key innovations included the use of a spectrogram algorithm in pre-processing, which transformed image data into a more detailed representation of surface defects, and the Parallel Attention-Residual CNN (PARC) model, which combined attention mechanisms for highlighting critical regions and residual connections for preserving important feature information. Unlike traditional models that relied on softmax for classification, this approach extracted deep features and evaluated them using multiple classifiers, with the SVM yielding the best results. Additionally, the INCA feature selection algorithm reduced the computational complexity while improving the classification accuracy. This tailored methodology enhanced defect detection by focusing on capturing subtle pixel variations and optimizing performance for the specific task. Commonly used CNN models in both datasets include either pre-trained CNN models, such as ResNet, or lightly weighted CNN models. The network file sizes of the proposed PARC model and other popular pre-trained models with weights are given in Table 10. The PARC model has less weight than the other pre-trained CNN models, except the MobileNet model. Therefore, the execution time is optimal for both training and testing. In addition, accurate classification is more important than speed in the detection of steel surface defects. Table 6 presents the classification accuracy results for both multi-class and binary classification tasks using various image enhancement techniques followed by CNN-based classification. The proposed spectrogram-based method shows superior performance in both scenarios. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Author** & **Method** & **Accuracy (\%)** \\ \hline [PERSON] et al. [54] & Fused Attention CNN model & 89.3 \\ [PERSON] et al. [55] & SegNet + CNN & 89.6 \\ [PERSON] et al. [56] & Deep CNN & 99.05 \\ [PERSON] et al. [14] & Transfer learning (ResNet) & 99.0 \\ [PERSON] et al. [15] & Lightweight CNN & 99.61 \\ Proposed approach & PARC, NRMI, SVM & 99.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracies of studies and the proposed model using the NEU Surface Defect Database. The experimental results demonstrate the effectiveness of the proposed spectrogram-based method in enhancing the discriminability of surface defects for both multi-class and binary classification tasks. While the baseline approach using raw images with CNN achieved a 93.0% accuracy for multi-class and 87.2% for binary classification, applying conventional image enhancement techniques such as histogram equalization, gamma correction, CLAHE, unsharp masking, and log/power-law transforms led to modest improvements. Among these, CLAHE and histogram equalization performed relatively better due to their ability to improve the local and global contrast, respectively. However, the proposed method outperformed all the others, achieving the highest classification accuracy of 93.5% in the multi-class scenario and 89.2% in binary classification.