# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 1 Introduction
These studies demonstrate the evolving focus from basic defect recognition to more sophisticated feature extraction and model optimization. Quantification of defects in images typically involves critical steps such as threshold segmentation, histogram analysis, and structural optimization. [PERSON] et al. [14] proposed an adaptive threshold method based on Adaptive cuckoo search optimization, coupled with genetic algorithms to determine optimal Gabor filter parameters for fabric defect extraction. [PERSON] et al. [15] systematically analyzed how grayscale quantization and window size affect the performance of grayscale co-occurrence matrix features in fabric defect detection. Meanwhile, [PERSON] et al. [16] applied threshold segmentation in medical imaging to quantify breast density for cancer risk assessment, while [PERSON] [17] developed a color space-based framework for the quantitative evaluation of flame stability. [PERSON] et al. [18] proposed an acne grading method and severity assessment index based on lesion classification, and constructed a lightweight model called Acne RegNet, which achieved high-precision acne lesion recognition and severity assessment close to the level of dermatologists. [PERSON] et al. [19] developed an automatic acquisition system and image processing algorithm for rice leaf images, which achieved estimation of leaf infection area percentage based on pixel statistics. These works underscore the importance of accurate quantification for enhancing detection system practicality, though they also reveal inherent limitations in image-based severity assessment due to variations in background-target contrast and lighting conditions. To address these challenges, researchers have extensively studied image-enhancement techniques [20]. Gray-level histogram equalization has emerged as a pivotal method for improving image quality and analysis reliability. [PERSON] et al. [21] proposed a threshold segmentation algorithm based on the statistical curve difference method to solve the problem of multi-target image segmentation with a small target area and grayscale close to the background. [PERSON] et al. [22] optimized adaptive histogram equalization through real-coded genetic algorithms and war strategy optimization, effectively enhancing image clarity. [PERSON] [23] proposed a constrained histogram equalization approach with adjustable contrast parameters to overcome shortcomings in traditional methods. [PERSON] et al. [24] further developed a variable contrast and saturation enhancement model using contrast-limited histogram equalization for local contrast correction. In low-light environments, [PERSON] et al. [25] introduced a dual-approach enhancement method combining histogram equalization with adaptive gamma correction in HSI color space, effectively resolving brightness inhomogeneity issues. [PERSON] et al. [26] proposed an adaptive high grayscale image enhancement algorithm based on logarithmic mapping and simulated exposure, which effectively improves the visual quality and contrast of high grayscale low contrast images. These advancements in image preprocessing highlight the necessity of robust enhancement strategies to enable accurate detection and evaluation in image detection. This study proposes a quantitative evaluation metric to assess the severity of surface fuzz defects in carbon fiber prepreg. Most deep learning-based object detectors output rectangular bounding boxes that only indicate the approximate location of defects. These bounding boxes do not provide information about the shape or severity of the defect. To address this limitation, the proposed method incorporates traditional image processing techniques, including adaptive thresholding and histogram normalization, to extract the actual defect region and suppress the background within the detection box. The contributions of this study can be summarized as follows:  
1. Select pixels of fuzz defects from the detection boxes bounded by the deep learning models;
2.