# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 1 Introduction  
Carbon fiber prepreg is a pre-impregnated sheet material fabricated by impregnating a polymer matrix into reinforcing fibers, serving as an essential intermediate for composite materials [1]. As a critical substrate for high-performance composites, defects in the prepreg--often introduced during the production process--can significantly lead to the unreliability of the final product [2]. These defects are usually divided into six categories: fuzz, wrinkles, poor glue, seam separation, foreign objects, and resin-rich areas. They have a substantial adverse effect on the mechanical properties, interfacial bonding, and long-term durability of composite structures. Among these, fuzz defects exhibit considerablemorphological variability in the prepreg. Their colors are influenced by different material backgrounds and lighting conditions. Their shapes are affected by the distribution of carbon fibers, manifesting as clustered, strip-shaped, or dispersed patterns. Although deep learning techniques have shown remarkable progress in industrial defect detection, they still face notable technical challenges in accurately evaluating the severity of fuzz defects in carbon fiber prepreg. Traditional machine vision systems are either limited to defect type recognition [3] or lack the ability to grade and assess the severity of defects, which these are two crucial needs for refined quality inspection in industrial production. Consequently, the urgent need for quantitative severity metrics of fuzz defects in prepreg manufacturing has prompted the development of evaluation methods to classify surface imperfection criteria and enable precise inspection systems. Surface defect detection based on image processing typically encompasses three categories: traditional image processing algorithms, machine learning-based methods, and deep learning approaches [4]. Faced with the demand for defect detection in carbon fiber prepreg, researchers have proposed diverse strategies to address the challenges of defect identification. For instance, [PERSON] et al. [5] developed a vision-based automatic recognition system that leverages an artistic conception drawing revert algorithm, exploiting repeated surface pattern features for defect identification, matching, and classification. Similarly, [PERSON] et al. [6] introduced a structural-constrained low-rank decomposition method for fabric defect detection, which constructs a fused image by extracting energy features from both the original and energy images to enhance defect visibility. [PERSON] [7] proposed a multi-level threshold image segmentation method based on the Black Widow Optimization algorithm, which effectively reduces computational costs by using Otsu or Kapur as the objective function. [PERSON] et al. [8] proposed a new edge detection method based on fuzzy divergence and fuzzy entropy minimization, and introduced an adaptive S-shaped fuzzy membership function and a new fuzzy degree quantization index, effectively improving the edge detection performance of grayscale images under fuzzy conditions. [PERSON] et al. [9] further combined low-rank decomposition with a structured graph algorithm, segmenting images into defect and defect-free blocks through local feature analysis while incorporating an adaptive threshold mechanism based on cycle counts during block merging. Neural network-based deep learning models have also been applied to defect detection [10]. [PERSON] et al. [11] introduced the Swin Transformer module into the YOLOv5 framework, significantly improving the detection of small-scale defects on fabric surfaces. [PERSON] et al. [12] proposed an improved YOLOv5 model, YOLO-HMC, which combines modules such as HorNet, MCBAM, and CARAFE to effectively improve the detection accuracy and efficiency of small defects on PCB surfaces. Building on this, [PERSON] et al. [13] optimized the YOLOv8 architecture by introducing Deformable Large Kernel Attention and Global Attention mechanisms, achieving substantial accuracy gains for prepreg surface defect detection.