# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 3 Results  
### Image Acquisition  
In this study, real-time image acquisition of carbon fiber prepreg was conducted on the production line using a linear array camera. The images had a resolution of 4096 \(\times\) 4000 pixels and a spatial resolution of 0.06835 mm per pixel, corresponding to a physical field of view of 280 mm \(\times\) 273.5 mm. The actual prepreg size corresponding to each image with a pixel dimension of 4096 \(\times\) 4000 is 280 mm \(\times\) 273.5 mm. The collected prepreg images were analyzed using a deep learning network to detect potential fuzz defects. Specifically, the regions identified within the detection bounding boxes are classified as fuzz defects and used as the examples for severity evaluation in this work.  
The minimum image resolution required for the effective application of the method is determined by the pixel dimensions corresponding to the smallest defect size that needs to be detected in the specific industrial context. For example, if the minimum acceptable defect size is \(\text{Zmm}\times\text{Zmm}\) and the spatial resolution is 0.0685 mm/pixel, then the minimum defect size in terms of pixels would be approximately \(\text{Z}/0.06835\) pixels \(\times\) Z/0.06835 pixels. Figure 1a displays the raw images captured by the camera, while Figure 1b presents the same images with annotations indicating the detected fuzz defects. Each bounding box is labeled with its width, height, threshold, and confidence level, providing a visual reference for the detection performance of deep learning models. In this study, the YOLOv8s architecture was employed as the detection network in the experimental setup. The model was trained on a dataset comprising 1270 annotated samples of fuzz defects. A total of 400 training epochs were conducted to ensure the model's convergence and robust performance in defect detection.  
Figure 2 presents an original image containing a linear object and an enlarged view of the region enclosed in the red box. Fuzzy defects are usually composed of dark linear objects and light flocculent objects. As observed in Figure 2, the average width of a linear object is 3-5 pixels. When the center of the convolution kernel is aligned with the center of this linear object, a small kernel size may result in the kernel covering only the dark linear region. This leads to the average grayscale value being computed based solely on the dark area, even though the corresponding point is judged as a defect-free area. To avoid this misclassification and ensure accurate detection of defect pixels, the width of the convolution kernel must exceed the width of the linear defect. That is, the kernel size (2p + 1) should be greater than five pixels, implying that the minimum value of \(p\) should be 3. The effectiveness of different \(p\)-values is illustrated in the following figures.  
Figure 3a shows the original grayscale image. Figure 3b,c displays the masks obtained for \(p=3\) and \(p=6\), respectively. As shown, increasing the value of \(p\) leads to a decrease  
Figure 1: (**a**) Images captured by the camera. (**b**) The image contains annotations of detection results obtained through neural network detection.  
Figure 2: Enlarge the local area of the original image.  
in the accuracy of edge extraction, with more background pixels near the boundary of the defect being incorrectly classified as defect pixels. Therefore, \(p=3\) is selected as the optimal parameter for subsequent calculations.