## 3 Experimentation and Evaluation
### Validation of Methodological Superiority  
In the preliminary phase of our study, we conducted comparative experiments to evaluate the improvement in accuracy and reliability of defect detection on textured materials using our proposed composite-enhanced defect detection method. To validate the superiority of the method proposed in this paper, we used the same batch of textured materials as experimental subjects and compared the defect information enhancement and scratch defect detection performance of our method with mainstream, commonly used defect detection methods. Given the significant technical differences between our method, which is based on traditional machine vision technology, and methods based on deep learning, we did not consider deep learning methods in our comparative trials. The comparative trials were divided into two parts:  
Comparison of Two-Dimensional Detection Methods: We compared our method with automatic threshold segmentation [12; 46] and fast Fourier transform based on Gaussian filtering (FFT-Gauss) [47]. Automatic threshold segmentation can achieve the fastest binary segmentation of an image; however, due to the influence of the material's texture, it fails to distinguish between scratches and the texture on the surface of the material. The FFT enhances the high-frequency information of the image, but the FFT alone tends to enhance both scratches and texture simultaneously and is affected by the lighting angle, making it difficult to fully observe the scratches. The limitations of these methods in detecting defects on textured materials are evident, as they often fail to distinguish between scratches and surface textures. For clarity, the detection results are shown in the first three columns of Figure 10.  
Comparison of Three-Dimensional Detection Methods: Given the limitations of two-dimensional methods, we also conducted a comparison with three-dimensional detection methods. We evaluated the performance of our method against photometric stereo vision alone [48; 49] as well as a combination of photometric stereo vision and FFT-Gauss [50]. The results of these comparisons are displayed in the last three columns of Figure 10.  
Using photometric stereo vision overcomes the effect of a single lighting angle, allowing for a full observation of the scratches, but it does not filter out the texture to a degree that allows for a clear distinction between scratches and texture. The photometric stereo vision + FFT-Gauss method, while effective in detecting anomalous scratch defects, exhibits significant inaccuracies due to its inability to mitigate the influence of the background texture, frequently confusing inherent texture variations with actual defects. To further validate the superiority of our method, we provide a more comprehensive comparison of the performance metrics in Table 2. As shown in the table, our method consistently demonstrates lower detection errors and a higher coverage of the original scratch areas when compared to the other methodologies. As the data in Table 2 show, our method consistently outperforms the alternatives in terms of correct rate, false positives, and scratch detection length error. The results clearly indicate that our method, which combines optimized photometric stereo techniques with FFT-Gabor filtering, successfully mitigates the influence of background texture and provides a more accurate detection of scratch defects on textured surfaces. Our method displays lower detection errors and a higher coverage of the original scratch areas. These findings support the conclusion that, for the detection of scratch defects on textured materials, our method, which builds upon optimized photometric stereo techniques and integrates FFT-Gabor filtering, overcomes the shortcomings of conventional Gaussian filtering in differentiating between surface texture and anomalous defects.