## 2 Literature Review on Yarn Quality Analysis
### Comparative Analysis of Existing Systems  
A comparative analysis between all the studied systems referred to in this section is presented in Table 1.  
By analyzing Table 1, some observations based on the features listed can be identified, namely:  
**(a)**: **Mechatronic Prototype Development:** System A has developed a mechatronic prototype. Systems B, C, and D do not have this feature.
**(b)**: **Non-destructive Prototype:** none of the systems have a non-destructive prototype.
**(c)**: **Yarn Winding and Unwinding System:** none of the systems include a yarn winding/unwinding capability.
**(d)**: **Image or Video Analysis:** All systems use image analysis. None uses video.
**(e)**: **Use of Vision System or AI:** Systems B, C, and D combine vision systems with AI, without deep learning techniques.
**(f)**: **Detect Defects:** Systems A and E use only vision systems, while B, C, and D combine them with AI.
**(g)**: **Specific Yarn Quality Parameters:**  
System A detects thick spots, thin spots, neps, the yarn diameter, and hairiness coefficient.  
System B detects the neps and mass variation coefficient.  
System C measures elongation at the break and tenacity.  
System D measures thick places, thin places, neps, the linear mass, coefficient of variation of mass, elongation at the break, and tenacity.  
System E measures the lengths of the crossed fibers.  
In summary, there is no system capable of obtaining most specific yarn quality parameters.
**(h)**: **Integration in production lines:** only Systems A and B are integrable into production lines. C and D depend on the USTER TESTER 3.
**(i)**: **Online or offline image acquisition:** Systems A and B allow online image acquisition; C, D, and E are only offline.
**(j)**: **Spectral analysis based on image processing:** none of the systems perform image-based spectral analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **A [39]** & **B [41]** & **C [37]** & **D [38]** & **E [40]** \\ \hline Mechatronic prototype developed? & ✓ & - & - & - & - \\ Non-destructive prototype? & - & - & - & - & - \\ Yarn winding and unwinding system? & - & - & - & - & - \\ Image or video analysis in yarn? & Image & Image & Image & Image & Image \\ Use of Vision System (VS) or Artificial Intelligence & & & & & \\ (AI) to detect defects in textile fabric or yarn & & & & & \\ Yarn twist orientation & - & - & - & - & - \\ Yarn twist step & - & - & - & - & - \\ Thick places & ✓ & - & - & ✓ & - \\ Thin places & ✓ & - & - & ✓ & - \\ Neps & ✓ & ✓ & - & ✓ & - \\ Yarn Diameter & ✓ & - & - & - & - \\ Linear mass & - & - & - & ✓ & - \\ Volume & - & - & - & - & - \\ Number of cables & - & - & - & - & - \\ Number of loose fibers & - & - & - & - & - \\ Mean deviation of mass U (\%) & - & - & - & - & - \\ Coefficient of variation mass CV (\%) & - & ✓ & - & ✓ & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features comparison between yarn quality analysis systems.  
* **Use of deep learning techniques to detect defects:** none of the systems use deep learning techniques to detect defects and also specifically to detect and classify yarn hairiness.
* **Yarn Hairiness Analysis:** only System E analyzes yarn hairiness, but without covering other parameters.