## 5 Assessing Dataset Availability and Practicality
### Fabric Pilling Detection Datasets  
Fabric pilling is a common issue in textiles that affects overall appearance and durability. Detecting and evaluating fabric pilling is crucial for quality control in the textile industry. The industry standards rely on human judges to rate the fabrics on a scale from one to five. The automation of pill detection by AI could facilitate a pilling rating system.  
Various datasets have been developed to aid in the detection and classification of fabric pilling, utilizing different methodologies and standards.  
Some datasets are based on standard pilling images provided by institutions such as the Suzhou Institute of Science; however, they were created using multiple replicates of a single type of fabric, in this case a 20/80 polyester/cotton twill weave [56]. However, some datasets only examined a limited number of fabrics, e.g., in the case of the dataset in [58], in which four colored and two lightly colored knitted samples were used. No other fabric specifications such as fiber type were provided.  
Another dataset [49] typically included images of woven fabrics with pilling levels ranging from 1 to 5, as per standards like GB/T 4802.1:2008 [61]. In addition to standard datasets, nonstandard pilling images are also collected using fabric pilling instruments like the YG502B. This study employed various data augmentation methodologies for generating \"nonstandard\" images, which are essential for training deep convolutional networks in pilling grade classification. Techniques such as multi-angle rotation (\(-45^{\circ}\) to \(+45^{\circ}\)), random cropping, random flipping (horizontal and vertical), and adding Gaussian noise are utilized. These methods simulate diverse fabric orientations, wear patterns, and real-world imperfections. Together, they significantly expanded the dataset to 1650 images, enhancing its representativeness of real scenarios without tampering with the number of pills in a certain area of the images [56].  
With advancements in deep learning, various datasets have been developed to train models for automatic pilling detection and classification. For example, a dataset [54] was compiled to evaluate the effectiveness of a dual-attention U-Net model for fabric pilling detection. This dataset includes 100 different fabric images with pilling grades of 1-4 only. Fabrics with a pilling rating of '5' were excluded because they lacked any pills which in turn were marked. The 100 images were then used to generate a dataset of 1000 using a data augmentation strategy to increase the number of samples by applying various geometric transformations. Similarly, [PERSON] et al. [50] utilized two datasets procured from a manufacturer located in Taiwan, which included fabrics with pilling ratings ranging from 2 to 5, according to ISO 12945-2:2020 [62], using the Martindale wear tester. The initial dataset encompassed 320 images, with 80 records allocated to each rating, whereas the subsequent dataset consisted of 1600 images, with 400 records corresponding to each rating. No other details of the fabrics were provided.  
Some datasets were very large but with limited applications, such as the dataset used by [PERSON] et al. [52] which included 64,109 images of woven, knitted, and nonwoven fabrics tested using the pilling box method. Whilst the dataset contained a very large number of images, the total number of different fabrics examined was only six in total, comprising either polyester or a wool or polyester blend, with around 10,000 images of each fabric. Similarly, [PERSON] et al. [51] created another dataset of 42,304 pilling images of four different knitted and nonwoven wool and polyester fabrics using the pilling box method. This dataset also classified hairballs into large, small, and tangled configurations for a detailed analytical approach. In a similar vein, [PERSON] et al. [55] concentrated on hairball detection, and while they used images of cotton, hemp, and wool fabrics, no other details about the fabrics or number of images were provided.