# Enhanced Curvature-Based Fabric Defect Detection:
## 6 Result and Discussion  
The images of defective fabrics were captured using an advanced camera-optic system. The identification algorithm was developed and tested using different approaches. Experimental results were analyzed, and the performance of various methods was evaluated. Defective fabrics collected from knitting factories were integrated into the in-house test setup, with the operating environment designed to simulate production sites. The captured images contained different physical defects, such as needle breakage, Lycra issues, and oil stains. The system classified the fabrics as either defective or non-defective, without specifying the type of fault. Subsequently, the confusion matrix and performance scores were generated for the different approaches. The results for each type of defect are presented in Table 5 for all models. Based on these findings, it is possible to discern which model is more effective in identifying specific types of defects. The experimental test results are summarized in Table 6, providing a comparison of accuracy and precision values. Figure 19: Fabric defect detection and label with Cnn. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Defect Type** & **Hole** & **Needle Break** & **May** & **Lycra** & **Oil** \\ \hline CA & 0.42 & 0.29 & 0.27 & 0.32 & 0.40 \\ MDCA & 0.78 & 0.82 & 0.74 & 0.81 & 0.60 \\ GT & 0.81 & 0.99 & 0.99 & 0.85 & 0.78 \\ CNN & 0.96 & 0.98 & 0.98 & 0.85 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Defect Success Rate: CA (Curvature Algorithm), MDCA (Modified Curvature Algorithm), GT (Gabor Transform), CNN (Convolutional Neural Network). Figure 18: Fabric image dataset with defects. Since the defects in the machine are 1 mm or larger, a minimum of 4 pixels must be captured within a 1 mm square area to detect the defect. When this requirement is calculated alongside the daily capacity of the machine, it results in 50-60 GB of data that must be processed and stored per day. Given the real-time study objective, the proposed discrete curvature algorithm is inferred to be more advantageous than learning-based algorithms in terms of the volume of data that needs to be processed and recorded. Based on the test results for the faulty images, instead of storing 57,600 bytes of data for a 96 \(\times\) 200 pixel image, it is more efficient to store 5100 bytes of extracted data from 638 points by considering the angle values, pixel positions, and radii. This approach significantly improves the compression rate and enhances real-time capability. The discrete curvature algorithm proposed for faulty fabric detection was applied to faulty fabric images, and the results obtained through specific developments for this problem are presented. By calculating the degree of curvature for each point, the system determined whether the curvature was concave or convex, and an updated discrete curvature function was obtained through an intelligent selection algorithm. It has been demonstrated that this approach can be effectively used for defect detection by leveraging efficient external radius and angle values. The Gabor transform achieves a high success rate in detecting vertical and horizontal defects with parameter settings specific to these types of faults. However, despite these successful results for vertical and horizontal defects, it does not yield satisfactory results for diagonal and other types of defects. Parameter adjustments are required to maintain performance across different fabric types. Fabric defect detection using learning-based methods requires high computational performance and substantial training data compared to other approaches. The highest accuracy was achieved when pre-trained models were used. High-performance hardware is essential for local defect detection, and when these hardware requirements are met, the learning-based method achieves high success rates.