Data in Brief

This paper introduces an online dataset focused on detecting hairiness in yarn, including loop and protruding ﬁbers. The dataset is designed for use in assessing artiﬁcial intelligence algorithms. The dataset consists of 684 original images. Through augmentation, this number increases to 1644, with 11,037 annotations derived from videos featuring 56.4tex purple cotton yarn. The videos were captured during the winding and unwinding processes of the purple yarn coil. An image acquisition system capable of capturing high-resolution images while the yarn is in motion was used, reaching speeds of up to 4.2 m/s and producing images with a resolution of 1.6M pixels. This dataset containing 100m of purple cotton yarn images was recorded and is available for down-load in various formats, including, among others, YOLOv8, YOLOv5, YOLOv7, MT-YOLOv6, COCO JSON, YOLO Darknet, Pascal VOC XML, TFRecord, CreateML JSON. Within an interface developed for a designed mechatronic prototype, users can choose to gather images or videos of yarn. Various characteristics of the yarn, such us: diameter, linear mass, volume, twist orientation, twist step, number of cables, hairi-ness index, number of loose ﬁbers, thin places, thick places, neps (mass parameters) and U, CV and sH (statistical parameters) can be obtained. Recently, this online yarn spinning

dataset was employed to validate artificial neural network models for real-time detection of hairiness in yarns, including loop fibers and protruding fibers.The dataset presented, with its clear annotations and wide array of augmentation techniques, serves as a foundational resource for prospective studies in textile engineering, enabling progress in the analysis and comprehension of yarn analysis. ©

Value of the Data
• Comprehensive Annotation: Contains 684 original images, expanding to 1644 with augmentations, focusing on hairiness (loop fibers, protruding fibers) in 56.4tex purple cotton yarn.• High-Resolution Relevance: Captured during winding and unwinding processes at speeds up to 4.2 m/s, providing a diverse collection of high-resolution images showcasing different yarn scenarios and conditions.• Multi-Format Adaptability: Available in various formats (YOLOv8, YOLOv5, YOLOv7, etc.), ensuring compatibility across different AI frameworks and platforms for increased accessibility and usability.• Mechatronic Prototype Integration: Integrated into a mechatronic prototype for extracting multiple yarn characteristics such as diameter, linear mass, volume, twist orientation, twist step, number of cables, hairiness index, loose fibers, thin places, thick places, neps (mass parameters), and statistical parameters (U, CV, sH).• Practical Application in Textile Industry: Enables real-time defect detection in yarns, validating and enhancing neural network models for effective identification and characterization of hairiness.
• Significant Contribution to Research and Industry: With clear annotations, statistical insights, and diverse augmentation techniques, it serves as a foundational resource for advancing research in textile engineering and AI-driven hairiness analysis, empowering researchers, engineers, and practitioners in textile sciences and AI for innovative insights in defect analysis, quality assessment, and industry advancement.

Background
The curated dataset assumes a crucial role in advancing standards within the textile industry by focusing on the pivotal importance of yarn quality.Recognizing the challenges faced by textile companies, particularly in procuring yarn from overseas suppliers, the dataset serves as a proactive solution to address these critical issues [1][2][3][4] .During the yarn manufacturing process, factors such as irregularities and dirt on the rollers of textile machines can decrease the quality of the yarn produced throughout the system [5][6][7][8] .In the textile industry, the quality of the final product is directly related to the quality of the yarn.Costs and complaints due to foreign fibers can be avoided by creating a quality management system to eliminate or minimize the number of foreign fibers in the yarn.Continuous inspection guarantees a constant and satisfactory quality of the final product [9][10][11][12] .However, the undesirable presence of hairiness, such as protruding and loop fibers, can compromise the quality of the yarn and, consequently, the final fabric.To ensure that fabrics achieve high-quality standards, strict quality controls and advanced inspection technologies are applied.Yarns with unwanted hairiness are detected and removed, ensuring that only high-quality yarns are used in the weaving process [9][10][11][12] .This emphasis on yarn quality control is crucial in controlling the processes used to obtain textile products, as loop and protruding fibers can cause several defects and problems that affect the quality and performance of the textile product [13][14][15] .To ensure the quality of yarns and minimize these issues, it is essential to implement proper production practices, such as quality control during the manufacturing process, careful selection of raw materials, and the adoption of advanced processing technologies to reduce the number of loops and protruding fibers [5][6][7][8][9][10][11][12] .Additionally, the use of automatic inspection systems during production can help identify and remove yarns with defects before they cause problems in the final products.This comprehensive approach to yarn quality management, supported by the insights provided by the curated dataset, contributes to enhancing overall product quality, improving customer satisfaction, and ensuring the financial stability of textile companies, ultimately bolstering the industry's standing and resilience [10][11][12][13][14][15] .In summary, the curated dataset, born out of the necessity to detect loop and protruding fibers, along with the developed artificial intelligence algorithm, enables the detection of these defects that cause immense problems in the quality of textile yarn and, consequently, the final quality of the fabric or textile product.

Data Description
There is no other dataset available that exhibits the same distinctive characteristics as our dataset of loop fibers and protruding fibers.Haleem et al. [16] and Ahmed and Uddin [17] , are undoubtedly valuable contributions to the field of textile fiber analysis.However, it is important to note that these datasets focus on different aspects or types of fibers and may not directly address the specific classes of loop fibers and protruding fibers.Our contribution fills this gap by providing a dataset dedicated exclusively to these fiber classes, which is crucial for advancing research in this specific area.This hairiness yarn dataset is truly unique and innovative, representing a significant contribution to the research community in the field of hairiness yarn, particularly focusing on protruding and loop fibers.To the best of our knowledge, there is no other dataset available that exhibits the same distinctive characteristics as ours.This dataset has been meticulously developed to address a gap in research, providing specific and detailed data on hairiness yarn, including the distribution and characteristics of protruding and loop fibers.
The impact of yarn hairiness on fabric quality is very important with increasingly strict control measures.High hairiness of the yarn affects the quality of the yarn and fabric, leading to visible defects such as stripes, fabrics and uneven garments.Furthermore, fabrics with such hairiness present greater tearing and thread breakage during sewing.Additionally, textile yarns with loop fibers and protruding fibers hinder accurate printing of patterns, resulting in distorted designs.Furthermore, high hairiness values of textile yarns contribute to increased hairiness of the textile fabric, compromising its final appearance.Finally, excessive hairiness of the yarn leads to increased air resistance and energy consumption during the manufacturing process.In response to these critical questions, specific label definitions have been established to address the nuances of loop fibers and protruding fibers in the textile context [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] .
This uniqueness makes our dataset a valuable tool for a wide range of applications and studies in the field of textile engineering, setting it apart from other available datasets.We recognize the importance of highlighting these aspects in our manuscript and will expand the discussion to further emphasize the uniqueness and relevance of our dataset compared to existing ones.
This dataset consists of images of purple cotton textile yarn with an average linear mass of 56.4 tex, captured with a magnification factor of 20x.A camera with a frame rate of 238 fps and a resolution of 1.6 million pixels was used in a specially designed mechatronic prototype (refer to Fig. 1 ) [18][19][20][21] .
The majority of images in the dataset depict two prominent defects in textile yarn hairiness: loops and protruding fibers.The dataset comprises images in JPG format, with each image depicting loops and protruding fibers in purple cotton yarn, as illustrated in Fig. 2 .
The dataset includes 684 original images, which were augmented to produce a total of 1644 images.These images were accompanied by 11,037 annotations derived from videos featuring 100 meters of purple cotton yarn.The videos were recorded and hosted in Roboflow [22] .
In Roboflow, the content of the .txtfiles in our dataset primarily comprises annotation information.
These files specify the coordinates of annotated objects, their locations within the image, and corresponding class labels.

Experimental Design, Materials and Methods
This dataset consists of images of purple cotton textile yarn with an average linear mass of 56.4tex captured with a magnification factor of 20x.It was used a camera with a frame rate of 238fps and a resolution of 1.6M pixels in a designed mechatronic prototype ( Fig. 1 ) [18][19][20][21] .
The suggested automated system designed for the identification of yarn hairiness comprises four primary phases: (1) Dataset image generation in mechatronic prototype.

Dataset Image Generation in Mechatronic Prototype
All images (JPG format) were captured using the vision system and they were taken under the appropriate lighting conditions to ensure the highest possible image quality.
This appropriate lighting conditions are: • 144 bright white LED lamps.
• Intense and focused illumination without shadows.
• Controlled and enclosed environment without light entry.
• Monochromatic light.• A yellow light filter based on cellophane paper was used over the LED ring.
• Adjustable brightness from 0 to 100 % with high brightness.
A Sentech USB 3.0 digital camera (OMRON, Japan) was employed, boasting specifications including: Resolution of 1.6M pixels; Monochrome; a Frame Rate of 238fps; Effective Pixels of 1440 × 1080 (HxV, Pixels); a Sensor Size of 1/2.9; a Cell Size measuring 3.45 × 3.45 (HxV, μm); equipped with a Lens Mount type C; and a Sensor IMX273.Additionally, the StViewer software from the same manufacturer facilitated image capture.The management of image acquisition and processing was overseen by an application or graphical interface developed in Visual Studio software in C# language, seamlessly interconnected with a high-resolution camera utilizing the OpenCV computer vision library, proficient in generating Mass spectrogram graphics.
The imaging speed for each yarn variety was selected so that all yarn segments were captured in continuous images without overlapping yarn segments between two consecutive images.This was achieved by synchronizing the image formation speed with the yarn unwinding speed, which depended on the parameters of the spinning process.Imaging speed (frames per second) and the yarn delivery speed (m/s) defined in the mechatronic prototype in the developed HMI interface were 11.33fps and 0.2 m/s, respectively.Each yarn video was recorded for a yarn length of 100 meters and saved in AVI video format without applying any image compression.

Dataset Annotation
This dataset not only foster the creation of machine learning models tailored for yarn hairiness detection but also pave the way for progress in the domain of artificial intelligence applied to textile sciences.It was categorized into two annotation classes: protruding fibers and loop fibers.Utilizing this dataset and its corresponding annotations, a series of tests and training exercises were executed to construct a robust model capable of real-time identification and classification of hairiness.LabelME [ 23 ] was employed in this process for annotation, with annotations created in polygonal mode.Fig. 3 exemplifies the annotations made within the dataset using LabelME.
In this dataset, Polygon Annotation was utilized for the detection of Loop and Protruding Fibers due to the following reasons [ 24 ]: 1. Precision and Accuracy: • Polygon annotation enables the precise labeling of loop fibers and protruding fibers, capturing their shapes and boundaries with high accuracy.This results in higher-quality training data for computer vision algorithms, especially when dealing with intricate and nonlinear fiber characteristics.

Flexibility and Versatility:
• Polygon annotation is adaptable to various shapes and types of fibers, including those with irregular and complex edges.This allows for the accurate identification of loop fibers and protruding fibers in images, regardless of their shape or orientation.

Handling Occlusion:
• In real-world scenarios, fibers are often partially obscured by other fibers or objects.Polygon annotation allows for the accurate labeling of only the visible parts of fibers, aiding machine learning models in handling occlusion in their predictions.4. Noise Reduction: • Polygon annotation eliminates the noise associated with bounding boxes, resulting in cleaner training data and more accurate models.This is especially crucial when detecting loop fibers and protruding fibers, where the presence of noise can compromise result quality.

Automation Potential:
• Modern annotation platforms semi-automate polygon annotation, simplifying the process by automatically detecting boundaries between fibers and other features in images.This significantly streamlines the labeling process, enabling more efficient detection of loop fibers and protruding fibers.
From the 684 images in the dataset, 70 % were allocated for training (480 images), 20 % for validation (138 images), and the remaining 10 % for testing (66 images).This dataset was generated without employing augmentation techniques [ 25 ].An average of around 16 annotations per image was obtained, with a total of 6733 annotations for loop fibers and 4304 annotations for protruding fibers ( Table 1 ).

Dataset Split in Roboflow
The images were pre-processed using Roboflow [22] , which allows users to build their computer vision applications with little knowledge.Resizing and enlargement pre-processing tech-  niques, such as saturation (adjustment within the range of -25 % to + 25 %), blur (applied to a maximum of 5 % of pixels), noise (added to a maximum of 5 % of pixels), and Flip (both horizontal and vertical flips), were applied in the dataset.All images were resized to 1440 × 1080 pixels and converted to grayscale (as shown in Fig. 4 ).
The process of applying augmentations to the images is carried out as follows:

Training Folder (Train)
The images undergoing augmentation are located in this folder.For each original image, three distinct versions are generated: 1.An image without augmentation.2. Two images with augmentation, varying according to the parameters defined for each image.
The augmentations are applied based on the specific parameters defined for each image, ensuring diversity and variation in the training data.The augmentations are random, varying always within the defined types previously.

Other Folders
In the remaining folders other than the training folder, no augmentations are applied.The images remain in their original form, without modifications or additional augmentations.This process ensures that only the images in the training folder are subjected to augmentations, while images in other folders remain unchanged.Having three distinct versions of each image in the training folder helps increase the diversity of the training data, which can be beneficial for the performance and generalization of the loop and protruding fibers detection model.
Considering that our dataset consists of two classes, "Loop fibers" and "Protruding fibers," and the augmentation techniques applied, we can adapt the response to provide specific justifications for each technique in the context of these classes.

Flip (Horizontal and vertical):
• Horizontal and vertical flips help introduce variations in the orientation and spatial arrangement of both loop fibers and protruding fibers.Loop fibers, for instance, may exhibit different curvatures and orientations, which can be effectively captured through horizontal flips.Similarly, protruding fibers may appear in various orientations due to their threedimensional nature, making vertical flips relevant for augmenting the dataset with diverse views.By applying flips, we enhance the model's ability to generalize across different orientations of both fiber types, thereby improving classification performance.2. Saturation Adjustment (-25 % to + 25 %): • Saturation adjustment allows for variations in the color intensity and richness of both loop fibers and protruding fibers.In textile images, variations in color saturation may arise due to differences in fiber composition, lighting conditions, or surface characteristics.By adjusting saturation within the specified range, we simulate these real-world variations, enabling the model to learn discriminative color features for both fiber types.This augmentation enhances the model's capacity to differentiate between loop fibers and protruding fibers based on color information, thereby improving classification accuracy.3. Blur (Applied to a Maximum of 5 % of Pixels): • Applying blur to a subset of pixels introduces local variations in image sharpness, which can be beneficial for capturing textural characteristics of both loop fibers and protruding fibers.In textile images, variations in fiber texture and surface smoothness are common and can affect visual perception.By selectively blurring a small percentage of pixels, we mimic these textural variations, enabling the model to learn robust features for distinguishing between different fiber textures.This augmentation technique enhances the model's resilience to variations in texture and surface smoothness, thereby improving its ability to accurately classify loop fibers and protruding fibers.4. Noise Addition (Maximum of 5 % of Pixels): • Adding noise to a subset of pixels introduces stochastic variations in pixel values, which can help simulate imperfections or irregularities present in both loop fibers and protruding fibers.In textile images, noise may arise from factors such as sensor noise, compression artifacts, or manufacturing imperfections.By incorporating noise into the images, we expose the model to realistic noise patterns, enabling it to learn to distinguish between signal and noise effectively.This augmentation enhances the model's robustness to noise and improves its ability to classify loop fibers and protruding fibers accurately in the presence of noise artifacts.

How to Download this dataset?
The dataset, Yarn Hairiness -Loop & Protruding Fibers, has been made available on the Mendeley public repository [8] .Interested users intending to utilize this dataset for noncommercial purposes are encouraged to cite this publication.The dataset can be accessed via the following link: https://data.mendeley.com/datasets/dkv6j6fw6c/1[ 26 ].
For a more comprehensive explanation of each step in our experimental workflow ( Fig. 5 ), we have outlined below a detailed breakdown of the acquisition process, elucidating the significance of each step and justifying their critical role in our methodology.

Metrics Results and Analysis with Dataset
In our research, we utilized a robust experimental setup, comprising essential components such as a Graphics Processing Unit (GPU) compatible with the CUDA parallel computing platform, alongside the PyTorch framework.Specifically, we employed the YOLOv5s6 deep learning model, designed for swift and precise object detection, leveraging the power of parallel computing provided by the GPU.This approach allowed us to accelerate computational performance, with the GPU facilitating parallel processing, while the PyTorch framework was instrumental in model construction and training.Our experimental setup involved an Ubuntu 22.04.2LTS operating system running on a system equipped with two Intel(R) Xeon(R) CPUs clocked at 2.00GHz, complemented by an Nvidia Tesla T4 GPU and 16 GB of RAM.On the software front, we utilized PyTorch version 1.13.1,CUDA 12.0, CUDNN 8700, Python version 3.7.9, and Google Colab [27] as the integrated development environment (IDE).
The training procedure commenced with the cloning of the YOLOv5s6 repository and the incorporation of the PyTorch [28] computer vision library within the Colab file.For a regimen of 100 epochs, it was achieved the best performance during training.
Building upon the foundational framework of the YOLOv5s6 Default algorithm, a refined and enhanced model, named YOLOv5s6 Yarn Hairiness, has been designed.This improved algorithm was developed explicitly for the purpose of detecting and classifying hairiness in yarn.The architecture of YOLOv5s6 Yarn Hairiness has been substantially augmented from its base model, integrating components and methodologies to optimize hairiness detection.These enhancements collectively contribute to a more sophisticated and precise yarn hairiness detection capability.
As post-training, it was conducted an evaluation of the model's training process, analyzing essential metrics such as the F1-score and the confusion matrix.The F1-score, recognized as a composite measure encompassing precision and recall, is a vital indicator of a model's accuracy on a given dataset.It is calculated as the weighted average of precision and recall, where precision denotes the ratio of true positive results to all positive predictions, and recall signifies the ratio of true positive results to the actual observations in the real class [29] .
The evaluation, showcased in Fig. 6 , portrays the F1-scores for both 'loop fibers' represented by the light blue line and 'protruding fibers' depicted by the orange line, achieving an F1 score of 0.67.F1-score is obtained by ( Eq. 1 ).
Additionally, it was observed the confusion matrix to comprehensively analyze the classification model's performance, namely: True Positives (when the model accurately predicted positives), True Negatives (when the model accurately predicted negatives), False Positives (erroneously predicting positives), and False Negatives (erroneously predicting negatives).Fig. 7 illustrates the generated confusion matrix for the object detection model, revealing that the YOLOv5s6 Yarn Hairiness model achieved an average accuracy of 65 % in predicting 'loop fibers' and an average accuracy of 75 % in predicting 'protruding fibers.'This comprehensive evaluation facilitated a thorough understanding of the model's predictive capabilities for different fiber classifications.
The YOLOv5s6 Yarn Hairiness model exhibits remarkable speed and precision when compared to the preceding version, YOLOv5s6 Default.Its classification process for a test image from the dataset takes merely an average of 0.009 s.As depicted in Fig. 8 , YOLOv5s6 Yarn Hairiness  adeptly identifies protruding fibers and looped fibers, outlining bounding boxes while providing confidence levels and appropriate labeling.The box plot represents how well the model performs in finding the center of an object and how well the predicted bounding box covers all objects.
Post-training and testing phases, the acquired weights from YOLOv5s6 Yarn Hairiness for our specific dataset were seamlessly transferred to the server.These weights serve a pivotal role in subsequent validation and inference processes.The graphs (left to right) in Fig. 9 show the three types of loss, namely, cash loss, objectivity loss, and rating loss (training and validation), as well as the metrics precision, recall and mean average precision obtained for the training epochs.
Observing Fig. 9 , plots (a-c) illustrate the box loss, object loss, and classification loss during the training phase of the YOLOv5s6-Hairiness model proposed in this study.Plots (f-h) depict the corresponding metrics for the validation set.Plots (d) and (e) showcase the precision and recall rates achieved by the model.Additionally, (i) and (j) present the mean Average Precision These metrics gauge the algorithm's ability to accurately predict object quality.Upon examining the plots in Fig. 9 , several observations can be made: • The three types of losses consistently decline throughout the training process.
• Toward the end of training, losses stabilize, although occasional spikes are observed.
• These curves reflect the model's performance in object detection.
• A value closer to 1 on the curves indicates higher confidence levels in the model's predictions.
The analysis of the results clearly indicates that the proposed YOLOv5s6-Hairiness model exhibits favorable outcomes, demonstrating good adaptability, high stability, and accuracy.The optimal model weights were obtained at the 100-epoch mark.
The proposed model outperformed other leading object detection frameworks, demonstrating higher accuracy compared to widely used alternatives.Training a YOLOv5 model with data augmentation resulted in significant performance differences.The results are summarized in Table 2 , showcasing improvements in key metrics with the proposed YOLOv5s6-Hairiness algorithm.
The dataset is available in multiple formats, catering to diverse machine learning platforms and frameworks.Through rigorous testing with various formats, including YOLO versions, COCO JSON, and others, the compatibility and performance implications were thoroughly assessed.
The comparative analysis in Table 3 illustrates the performance metrics comparison between the proposed optimized YOLOv5s6-Hairiness model and other models with data augmentation: These results highlight the superior accuracy achieved by the proposed YOLOv5s6-Hairiness model compared to other object detection frameworks, showcasing the impact of dataset format on model performance and compatibility across different machine learning platforms.This comprehensive evaluation aids in informed decision-making regarding dataset selection and framework utilization for object detection tasks.By incorporating these future developments and techniques, our dataset can continue to evolve, ensuring its relevance and impact in the field of textile engineering and AI-driven yarn quality assessment.

Limitations
• Size and Complexity: Loop fibers can be quite large and intricate, which poses a challenge for the model to capture all the necessary details required for accurate detection.• Diverse Appearances: The detected loops exhibit a wide range of shapes and appearances.
The model may struggle to generalize and identify all possible variations effectively.• Occlusions and Overlapping: Occlusions or the overlapping of loop fibers may obscure parts of them, making it more challenging for the model to accurately detect them.Addressing occlusions in object detection involves employing techniques to ensure that the model can accurately detect objects even when they are partially obscured by other objects in the scene.
Here's a technical explanation of strategies commonly used to mitigate the impact of occlusions: 1. Multi-Scale Detection: Object detection models often utilize multi-scale feature maps to detect objects of varying sizes within an image.These feature maps allow the model to detect objects at different scales, which can help in detecting partially occluded objects.2. Feature Fusion: Many modern object detection architectures incorporate feature fusion mechanisms that combine features from different layers of the neural network.This enables the model to capture contextual information and improve its ability to recognize objects, even when they are partially occluded.the model to a diverse range of occlusion scenarios, improving its ability to generalize to real-world situations. 5. Advanced Loss Functions: Designing custom loss functions that penalize detection errors caused by occlusions can encourage the model to learn robust representations that are less sensitive to occluded regions.6. Post-Processing Techniques: Post-processing steps such as non-maximum suppression (NMS) can help refine the detection results by filtering out redundant or overlapping detections, which can be particularly useful in scenarios with occlusions.• One potential limitation of our dataset is that the use of Polygon Annotation, while effective, may pose certain challenges.This method inherently involves an iterative process, and although LabelME software was selected as the most suitable option for annotating specific classes, there are other annotation tools available that could also be explored.Examples include Roboflow Annotate, CVAT, Make Sense, Labelbox, and Scale AI.Therefore, while our dataset represents a pioneering effort in this field, there is still room for future research to investigate alternative annotation techniques and software options.
These factors combined make the detection of loop fibers a complex and demanding task for the model.

Fig. 1 .
Fig. 1.(a) Prototype's mechanical system with camera; (b) Closed Prototype with HMI; (c) Interface created for Image acquisition; (d) User interface HMI -Screens corresponding to the "yarn quantity and speed" test (a) and the "time and speed" test (b) [18-21] .

Fig. 2 .
Fig. 2. Yarn hairiness image with loop fibers and protruding fibers.The image is of cotton yarn with an average linear mass of 56.4tex and was captured with a magnification factor of 20x.

Fig. 3 .
Fig. 3. Annotations of an image of the yarn made by LabelME (loop fibers-green color and protruding fibers-red color) [ 23 ].

Fig. 4 .
Fig. 4. Example of noise data augmentation in an image of the dataset (Cotton yarn with an average linear mass of 56.4tex, with a magnification factor of 20x) [ 25 ].

Fig. 7 .
Fig. 7. Confusion matrix for the proposed object detection model.

Fig. 9 .
Fig. 9.The graphs represent the performance of our model when trained and validated with low-resolution images.

3 .
Attention Mechanisms: Attention mechanisms in neural networks can help the model focus on relevant parts of the image while ignoring irrelevant or occluded regions.By dynamically adjusting the importance of different image regions, attention mechanisms can improve the model's robustness to occlusions. 4. Data Augmentation: Augmenting the training data with simulated occlusions can help the model learn to recognize objects even when they are partially obscured.Techniques such as random cropping, masking, or adding occluding objects to training images can expose

Table 1
Classification of the dataset.

Table 2
Metrics evaluating detection of yarn hairiness with augmentation were conducted employing both the default YOLOv5s6 algorithm and the enhanced YOLOv5s6 -Hairiness algorithm.
Enhance Annotation Quality : Implement regular quality assessments to increase the accuracy with the use of bounding box annotations.2. Increase Sample Diversity: Expand dataset to include a broader range of yarn types, materials, and hairiness characteristics for improved representativeness.3. Enhance Annotation Granularity: Provide detailed fiber characteristics (length, thickness, density) to enable precise AI analysis of yarn quality.4. Standardize Annotation Consistency: Implement clear annotation guidelines, training, and quality control processes to ensure dataset quality and consistency.5. Improve Annotation Quality: Conduct regular quality checks and peer reviews to enhance annotation accuracy and reliability.6. Update Dataset Responsiveness: Regularly update dataset to reflect advancements in textile technologies and AI algorithms for continued relevance.7. Address Occlusions Challenges: Explore advanced techniques like multi-scale detection and attention mechanisms to mitigate occlusion impacts on detection accuracy.8. Explore Alternative Annotation Tools: Investigate alternative annotation software options (e.g., Roboflow Annotate, CVAT) and techniques to enhance annotation efficiency and effectiveness.9. Collaborate with Industry Partners: Collaborate with textile industry stakeholders to collect real-world data and ensure dataset alignment with industry needs and challenges.