## 3 Results

Table 5: Overview of studies utilizing AI techniques for fabric surface assessment. Table 3 focuses on studies related to textile sorting and material classification, highlighting AI techniques used to distinguish between fiber types, material blends, and garment categories. Table 4 presents research on garment and fabric defect detection, showcasing AI-driven approaches for identifying faults such as broken stitches, damage in zippers, or general wear-and-tear. Finally, Table 5 compiles studies addressing surface-level fabric assessments, including pilling detection, fiber flaw identification, and fabric smoothness evaluation. Each table includes detailed attributes such as the study's focus, AI technique, dataset type, and fabric/fiber information. This structured layout facilitates clearer cross-study comparison and reveals domain-specific trends in dataset usage, model complexity, and fabric variability. Through this classification, we aim to enhance clarity and provide targeted insights into how AI is being leveraged across key aspects of clothing quality evaluation and textile reuse. For instance, across all three domains, CNNs are the most frequently used technique, consistently appearing in textile material recognition studies [19; 23; 26; 27], fabric defect detection [30; 36; 38], and surface-level assessments such as pilling detection [49; 56; 57]. This widespread use is attributed to CNNs' strong performance in image-based tasks including classification, detection, and segmentation. In addition to CNNs, YOLO (You Only Look Once) models such as YOLOv5 and YOLOv8 are commonly applied for fast and accurate object detection, particularly in garment defect detection scenarios [29; 30; 32; 41]. Furthermore, several studies have employed hybrid models, such as CNN-LSTM for combining spatial and temporal features [22; 45; 46] and DeepLabV3+ integrated with EfficientNet for multi-scale feature extraction and efficient segmentation [42; 43]. These models demonstrate the field's shift toward increasingly sophisticated AI architectures tailored to specific challenges in textile analysis. These tables also include information about the types of images within the datasets, such as what type of textiles were captured, which ranged from full garments to larger sections of fabrics, small switches of materials, and close-up or microscopic images of yarns and fabrics. The source of the images was also noted, as some studies sourced existing images while others created the images in their datasets, with both approaches often increasing the dataset size through image manipulations such as flipping or cropping. The reviewed textile sorting studies utilized both natural and synthetic fabrics, with cotton, polyester, wool, silk, and viscose being the most common, along with various  
Figure 4: Breakdown of research studies by the country associated with the first author’s institution, highlighting China’s dominant contribution with 25 papers, followed by smaller contributions from other countries such as South Korea, Portugal, Spain, and the United States. blended materials. Cotton-rich textiles appeared frequently, likely due to their high recycling value. Similarly, the reviewed studies on pilling identification (Table 5) predominantly utilized knitted fabrics, reflecting their common use in apparel susceptible to surface wear. Some studies extended their scope to include woven and nonwoven materials, while a few incorporated specific fibers such as wool, cotton, and hemp or considered variations in fiber content, fabric structure, and coloration to simulate real-world conditions. Nonetheless, several studies failed to report detailed fabric specifications, which poses a limitation for assessing the generalizability of AI models. This inconsistency underscores the necessity for standardized and comprehensive dataset documentation to facilitate robust model evaluation and applicability across diverse textile categories.