## 3 Results  
A total of 187 articles were initially identified from the selected online sources. Following the screening and selection process outlined in Figure 1, 49 studies met the inclusion criteria and were ultimately included in this review. Figure 1(a) categorizes the sources of research, showing that most studies were published in peer-reviewed journals, reflecting the high academic rigor and credibility of this area of investigation. Figure 1(b) illustrates the trend in publications over time, showing a steady rise as the years progress. Research activity reaches its peak in 2023 with 14 publications, likely driven by the growing availability and capabilities of AI technologies. This pattern reflects a significant surge in interest after 2020, fueled by advancements in AI and a heightened focus on sustainability and efficiency in the textile industry. The stabilization observed in recent years suggests that foundational research in this field may be maturing, potentially signaling a shift toward practical implementation and real-world applications for sorting and defect detection. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Domain** &
\begin{tabular}{c} **Risk of** \\ **Bias Judgement** \\ \end{tabular} & \multicolumn{1}{c}{**Rationale for Concern**} \\ \hline
1. Concerns regarding & & & \\ specification of study & Low & This review clearly outlined pre-specified inclusion and exclusion criteria that were well-aligned with the review objectives. \\
2. Concerns regarding & & The search strategy was systematic and involved multiple reputable databases, ensuring broad coverage of relevant literature. The process for both screening titles and abstracts and assessing full-text papers included multiple reviewers, reducing the likelihood of selection bias. \\
3. Concerns regarding & Low & Data extraction was conducted systematically using a structured form that ensured consistency across studies. Key methodological and technical characteristics were captured in detail. All articles were assessed independently by a minimum of two reviewers, and appropriate data were abstracted independently, minimizing the potential for bias and error. \\
4. Concerns regarding & Low & The findings were systematically synthesized according to the application domains—textile sorting, garment defect detection, and piling identification. This structure allowed for the identification of key trends, commonly used methods, and differences across each area. Limitations of individual studies, such as limited dataset availability, lack of image diversity, restricted real-world validation, and model complexity, were explicitly acknowledged and discussed. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ROBIS risk of bias assessment summary: this table presents the domain-wise evaluation of potential bias in this review using the ROBIS tool. Each domain was assessed based on signaling questions, with concern levels determined in accordance with ROBIS guidelines. Figure 3 shows that IEEE Xplore, Elsevier, and MDPI were the leading sources of articles in this review, each contributing a significant share and together accounting for over half of the total publications. Taylor & Francis (15.2%) and Sage Journals (10.9%) also made notable contributions, indicating their relevance to the review's focus. The \"Other\" category, which includes theses, also accounted for 10.9%, reflecting the inclusion of grey literature in the analysis. All remaining sources collectively made up the rest of the contributions, representing a smaller share of the overall distribution. Figure 4 provides a breakdown of research papers based on the countries associated with the first author's institution. China leads significantly with 25 papers, followed by South Korea, Portugal, and Spain, each contributing 3 papers. Other countries, such as the United States, Taiwan, and India, had smaller contributions, while several nations, including Pakistan, Finland, and Sri Lanka, have a single paper each.