## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Paper** & **Year** & **Study Foos** & **Technique** & **No Images** & **Dataset Availability** & **Dataset Type** & **Dataset Source** \\ \hline
[29] & 2023 & Fabric defect detection & YOLOv5 & 647 & public & garment (full) & created \\
[30] & 2023 & Garment defect detection & CNN & 800 & private & garment (detail) & created \\
[31] & 2024 & Fabric defect detection & YOLOv8 & 2800 & public & fabric (watch) & existing \\
[32] & 2024 & Fabric defect detection & GSI-YOLOv8 & 6345 & public & fabric (watch) & existing \\
[33] & 2022 & Garment inspection & Image processing & 174 & private & garment (full) & created \\
[34] & 2024 & Zipper tapsd defect detection & CNN and YOLOv5 & 1200 & private & trims (detail) & existing \\
[35] & 2023 & Clothing inspection for visually impaired & CNN & 11,604 & public & garment (full) & mixed \\
[36] & 2023 & Garments defect detection & YOLOv5D & 7021 & public & fabric (watch) & existing \\
[37] & 2024 & Fabric sewing heavy detection & U-Net Network & 1000 & private & garment (detail) & created \\
[38] & 2022 & Boken switch detection & CNN & 28 & private & garment (detail) & created \\
[39] & 2021 & Zipper tapsd defect detection & CNN & 11,201 & private & trims (detail) & created \\
[40] & 2024 & Garments damage detection & CV & 16,066 & public & garment (full) & created \\
[41] & 2023 & Garments defect detection & YOLOv5 & 3161 & private & garment (detail) & created \\
[42] & 2022 & Sewing switch detection & DeepLabV3-EfficientNet & 900 & private & garment (detail) & created \\
[43] & 2020 & Fabric defect detection & DeepLabV3+EfficientNet & 98,777 & public & fabric (watch) & created \\
[44] & 2024 & Fabric defect detection & DL and SVM & 3375 & public & multiple & created \\
[45] & 2020 & Fabric defect detection & LSTM-CNN & 12,000 & public & fabric (watch) & existing \\
[46] & 2023 & Fabric defect detection & LSTM-CNN & 4100 & private & fabric (watch) & created \\
[47] & 2019 & Fabric defect detection & YOLO and CNN & 245 & public & fabric (watch) & created \\
[48] & 2024 & Fabric defect detection & NN and SVM & 1300 & public & fabric (watch) & created \\ \hline \end{tabular}
\end{table} 
Table 4: Overview of studies utilizing AI techniques for defect detection, highlighting the year, study focus, applied techniques, number of images, dataset availability, dataset type, and defect types. The dataset specifies whether the images used for training AI models representing full garments or fabric sections of varying sizes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Paper** & **Year** & **Study Focus** & **Technique** & **Noc Images** & **Dataset Type** & **Dataset Source** & **Fabric/Fiber Type** \\ \hline
[49] & 2021 & Pilling assessment & CNN & 25,876 & fabric (switch) & created & Wooken limited \\
[50] & 2019 & Pilling classification & PCA and NN & 1920 & fabric (switch) & existing & Knitted \\
[51] & 2023 & Pilling assessment & LSWet & 42,304 & fabric (switch) & created & Wooven, knitted, and nonwoven \\
[52] & 2022 & Pilling assessment & SONet & 64,109 & fabric (switch) & created & Knitted, nonwoven fabric \\
[53] & 2022 & Pilling classification & Deep learning Net & 320 & fabric (switch) & created & Knitted \\
[54] & 2023 & Pilling area segmentation & U-Net network & 1000 & fabric (switch) & created & Not given \\
[55] & 2020 & Pilling detection & Cellular NN & 400 & fabric (switch) & unclear & Cotton, hep, and wool \\
[56] & 2023 & Pilling assessment & Saliency-based CNN & 1650 & fabric (switch) & mixed & Wooven fabrics \\
[57] & 2022 & Fiber flaw detection & CNN & 5268 & other & created & Not given \\
[58] & 2024 & Pilling assessment & UNet network & 153 & fabric (switch) & created & Varying filter contents, tissue \\  & & Fabric smoothness & & & & structures, and colors \\
[59] & 2020 & appearance assessment & CNN & 4620 & fabric (section) & created & Not given \\
[60] & 2021 & Fabric surface assessment & Deep learning & 7000 & fabric (microscopic) & created & Not given \\ \end{tabular}
\end{table}