# Quantitative Evaluation Method for the Severity of Surface Fuzz Defects in Carbon Fiber Prepreg
## 4 Discussion  
This article proposes a quantitative evaluation method for the severity of fuzz defects on the surface of carbon fiber prepreg. We selected 20 surface images of prepreg materials exhibiting varying degrees of fuzz defects, as identified and labeled by a neural network-based detection model with bounding box annotations. We have developed an algorithm for separating fuzz areas from detection bounding box selected images, which filters for defective pixels by comparing local grayscale values centered on pixels with a threshold. It can be observed from the experiment that the method used can effectively segment the defect area. Quantitative analysis and calculation were conducted on the defects of the fuzz. Based on the analysis and processing of grayscale histograms, the quantification of defect severity has been achieved, and the quantification results are not affected by different materials, color brightness of different areas, and lighting conditions in the photography environment. Sort the defect images according to the quantified values and match the results determined by human judgment. This quantitative method effectively solves the key problem of existing neural networks being difficult to dynamically adjust judgment criteria according to quality requirements in industrial defect detection. The ranking results obtained using the method proposed in this article have a similarity of 92% with the ranking results artificially obtained by experienced staff. This validates the effectiveness and reliability of the proposed method in assessing the severity of defects and provides an interpretable decision-making basis for automated quality inspection systems.  
Conceptualization, Y.L. ([PERSON]) and M.S.; methodology, Y.L. ([PERSON]); software, Y.L. ([PERSON]); validation, Y.L. ([PERSON]), M.S. and X.W.; formal analysis, M.S.; investigation, Y.L. ([PERSON]) and H.L.; resources, M.S. and T.L.; data curation, Y.L. ([PERSON]); writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, M.S.; visualization, Y.L. ([PERSON]); supervision, T.L.; project administration, M.S. and X.W.; funding acquisition, M.S. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Development funding of AVIC Composite Corporation LTD, grant number GC732011602.  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors acknowledge the technical cooperation and support of prepreg production workers for the artificial rating of the fuzz defect samples in the AVIC company.  
Author [PERSON] was employed by AVIC Composite Corporation LTD. The remaining authors declare that the re-search was con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Image Identifier** & **Manual Ranking** & **Algorithm Ranking** & **Rank Consistency@K** \\ \hline
16 & 16 & 17 & 15/16 \\
17 & 17 & 19 & 16/17 \\
18 & 18 & 18 & 17/18 \\
19 & 19 & 6 & 1 \\
20 & 20 & 20 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._