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This dataset presents the primary open-access assortment of near-infrared hyperspectral imaging (NIR-HSI) knowledge for the optical identification of textiles, with a give attention to supporting analysis in sensor-based textile sorting and recycling. The dataset contains hyperspectral photos, RGB images, and detailed metadata, together with fibre composition and color, for 71 post-industrial textile samples, collected in Finland. Over 11 million spectra are included within the hyperspectral photos, with greater than 6 million annotated, offering a sturdy basis for machine studying and knowledge evaluation. In addition, we offer a single consultant NIR spectra and RGB worth for every pattern with a purpose to accommodate traditional spectroscopic evaluation.
Used clothes have been sourced from a companion firm specializing in end-of-life textile administration, with floor fact data on fibre composition obtained from suppliers. Small items of every garment have been measured utilizing Specim SWIR 3 hyperspectral digital camera and photographed with high-resolution cell phone digital camera (Samsung Galaxy A52). The dataset is organized into folders containing uncooked and processed knowledge, together with ENVI-format hyperspectral photos, RGB photos, in addition to CSV information with imply spectra, imply RGB values, and pattern metadata. An instance Python script is supplied to facilitate knowledge entry and processing.
Potential reuse situations embody classification of textiles by materials or color, prediction of pure fibre content material, picture segmentation, algorithm improvement for spectral classification, and use as a reference spectral library. The dataset’s complete construction and open availability deal with the restrictions of earlier analysis, which regularly relied on small or private datasets, and is meant to speed up advances in optical identification applied sciences for textile recycling.
Keywords: Near infrared spectroscopy, Garments, Textile waste, Recycling, Sorting, Machine studying
Specifications Table
| Subject | Engineering & Materials science |
| Specific topic space | Optical identification of various clothes for sorting primarily based on varied options textile options corresponding to fibre composition and color. |
| Type of knowledge | Image, Chart, ENVI Raw, Processed |
| Data assortment | Used clothes have been collected by a companion firm specializing in end-of-life administration of post-industrial textiles. Ground fact data of the samples was gathered by the companion firm from their suppliers. Small items have been minimize from these clothes and have been measured utilizing near-infrared hyperspectral imaging (Specim SWIR 3) and images (Samsung Galaxy A52). |
| Data supply location | All the samples and knowledge have been collected in Finland. The knowledge is saved on the authors’ establishment. |
| Data accessibility | Repository title: Zenodo Data identification quantity: [DOI: 10.5281/zenodo.18269172] Direct URL to knowledge: [doi.org/10.5281/zenodo.18269172] Instructions for accessing these knowledge: Zenodo is free and open entry to make use of with out the necessity for registering or different particular necessities. |
| Related analysis article | none |
Textile recycling is presently closely beneath analysis and improvement to cope with the massive quantities of generated textile waste. Different recycling strategies have totally different necessities when it comes to what could be enter to every course of. Since the variability of fibres and fibre blends in textiles is vast, correct sorting based on fibre sort is required with a purpose to generate high-quality feedstock. Currently, textile sorting is completed manually, however with a purpose to scale-up the method, automated applied sciences are wanted. Arguably, one of the crucial promising expertise on this area is optical sorting, which is anticipated to interrupt by way of within the close to future. However, this expertise requires very excessive variety of reference pattern knowledge to have the ability to reply to the sorting necessities.
Our dataset introduced on this paper fills a vital hole by offering the primary open-access near-infrared hyperspectral imaging (NIR-HSI) dataset of textiles, supporting analysis and improvement in optical textile identification, sorting and recycling. NIR-HSI is seen as a key enabler in textile sorting on account of the truth that it could actually, primarily based on the spectral response, establish totally different fibre and fibre blends in a bit of textile. Since NIR-HSI knowledge comprises spatial dimension along with the spectral dimension, totally different garment elements made of various materials could be recognized as properly.
Our dataset is complete, because it comprises hyperspectral photos, images, and detailed metadata (together with fibre composition and color) for 71 post-industrial textile samples, with over 11 million spectra, greater than 6 million of that are annotated, enabling sturdy materials for machine studying and knowledge evaluation. The dataset could be reused for duties corresponding to classification of textiles by materials or color, prediction of pure fibre content material, picture segmentation, algorithm improvement for spectral classification, and as a reference spectral library for various textile supplies. Unlike most prior analysis, which used small or private datasets, this useful resource presents a big, well-documented, and overtly shared dataset, growing its relevance and affect for the scientific group and trade alike.
Researchers, optical sensor and sensor-based sorter producers, algorithm builders, waste administration firms and textile recyclers might profit from our dataset. For researchers, the dataset gives materials for spectroscopic evaluation. For sensor and sensor-based sorter producers, the dataset gives reference spectra which can be utilized to find optimum wavelength ranges for identification and thus number of applicable sensor expertise. For algorithm builders, the contained knowledge can be utilized for testing totally different pre-processing and machine studying strategies. Finally, for waste administration and textile recylers, the dataset gives data on the capabilities of the used sensor applied sciences in automated sorting.
Recycling is critical to cope with ever-increasing quantities of textile waste, but it surely requires correct sorting based on fibre sort, which is commonly finished manually. There has been rising curiosity in optical identification of textiles, enabling sensor-based sorting. Most revealed analysis articles on this area cope with very restricted pattern units, both when it comes to amount or variability. Moreover, the datasets are not often public, limiting their relevance.
Some open datasets associated to textiles can be found. NIST lately revealed near-infrared (NIR) spectra and optical microscopy knowledge of 64 virgin and post-consumer materials [1]. RISE revealed images of over 31000 post-consumer clothes from sorting facility [2]. UEF revealed seen hyperspectral imaging textile texture database [3]. TextileWeb comprises web-crawled photos of garments and related labels [4], and eventually, the dataset by Gil-Arroyo [5] is said to textile defect detection. To the very best of our information, these datasets are the one ones out there.
Near-infrared hyperspectral imaging (NIR-HSI) has lately gained recognition as an imaging modality. This technique is seen as a key enabler for sensor-based sorting, because it permits for pixel-by-pixel NIR evaluation over a big space. As of but, no NIR-HSI datasets of textiles are overtly out there. This article bridges this hole.
The dataset comprises hyperspectral photos and images of 71 post-industrial textile samples in addition to metadata of every pattern, together with the fibre composition and color. HSI in essence combines spectroscopy with spatial knowledge. In spectroscopy, a sensor is used to measure the absorbance of electromagnetic radiation by a pattern on totally different wavelengths, producing a spectrum. This absorbance depends on the molecular composition of the pattern. Organic molecules are very lively within the near-infrared vary, with near-infrared spectroscopy enabling identification of various natural elements in a pattern. As against spectroscopy through which a single level is measured, HSI measures a big space, with every pixel comprising of a spectrum.
The measurement of the dataset is roughly 11.8 gigabytes. It comprises over 11 million spectra, over 6 million of that are annotated. The red-green-blue (RGB) images comprise over 243 million pixels, of which over 26 million are annotated. The samples are labelled with “sample_NNNN”, for instance, “sample_15-1”. The dataset is structured as seen in Fig. 1. In this part, we give an in depth description of every file and folder.
Visualization of the dataset construction.
The particulars of this file are as follows:
Python script file
Contains an instance script of how one can learn into reminiscence every of the totally different information on this repository, in addition to how one can extract the area of curiosity (ROI) of RGB and NIR-HSI photos of an instance pattern.
Developed with Python 3.10.111, on 64-bit Windows 10
The particulars of this file are as follows:
Comma separated worth (csv) file, semicolon separator “;”
Contains data of every pattern, together with composition and look
Contains 72 rows (together with one header row)
Label: label of the pattern, in format “sample_NNNN”
Unknown or outlier?: determines whether or not the composition of the pattern is unknown (row worth equals “unknown”), or whether or not it’s an outlier primarily based on our knowledge evaluation (row worth equals “outlier”). If empty, the composition is understood and the pattern is just not deemed an outlier
Polyamide [%]: share of polyamide within the pattern
Viscose [%]: share of viscose within the pattern
Lyocell [%]: share of lyocell within the pattern
Modacrylic [%]: share of modacrylic within the pattern
Polyacrylic [%]: share of polyacrylic within the pattern
Wool [%]: share of wool within the pattern
Carbon fibre [%]: share of carbon fibre within the pattern
Polyurethane [%]: share of polyurethane within the pattern
Elastane [%]: share of elastane within the pattern
Polyester [%]: share of polyester within the pattern
Cotton [%]: share of cotton within the pattern
Color: predominant color of the pattern
Texture: If the pattern comprises textural options corresponding to stripes or grids, this data is given on this column. Otherwise, this column is empty
Extra data: Extra data of the pattern that doesn’t concern different columns
The particulars of this file are as follows:
Text file containing the library packages required for working the “example_file_reading.py” Python script
The packages could be put in with e.g. pip bundle installer utilizing the next command (with out citation marks): “pip install -r requirements.txt”
Used PIP model: 23.0.12
The particulars of this file are as follows:
Comma separated worth (csv) file, semicolon separator “;”
y_start, y_end = 564, 1180
x_start, x_end = 675, 1278
IMPORTANT: the pixel coordinates conform to the Python syntax, the place the beginning index is 0, and the tip column and row (y_end, x_end) are NOT included within the area of curiosity
Contains 72 rows (together with one header row)
Label: label of the pattern, in format “sample_NNNN”
R: imply R colour worth of the pattern
G: imply G colour worth of the pattern
B: imply B colour worth of the pattern
The particulars of this file are as follows:
Comma separated worth (csv) file, semicolon separator “;”
Contains 72 rows (together with one header row)
Label: label of the pattern, in format “sample_NNNN”
The remaining 288 columns comprise the reflectance values of every wavelength, within the vary 953.04 – 2547.64 nanometres; the wavelengths are enumerated within the header row
The particulars of this file are as follows:
Comma separated worth (csv) file, semicolon separator “;”
Contains the area of curiosity from which the imply NIR spectrum of every pattern is calculated
The imply values themselves are contained within the file “swir_mean_spectra.csv”
Contains 72 rows (together with one header row)
Label: label of the pattern, in format “sample_NNNN”
y_start: beginning y-coordinate of the area of curiosity
y_end: ending y-coordinate of the area of curiosity
x_start: beginning x-coordinate of the area of curiosity
x_end: ending x-coordinate of the area of curiosity
IMPORTANT: the pixel coordinates conform to the Python syntax, the place the beginning index is 0, and the tip column and row (y_end, x_end) are NOT included within the area of curiosity
This .zip archive3, named “RGB.zip”, comprises a folder named “RGB”. The particulars of this folder are as follows:
This .zip archive, named “SWIR-HSI.zip”, comprises a folder named “SWIR-HSI”. The particulars of this folder are as follows:
In this part, we define the used samples, knowledge assortment parameters, area of curiosity willpower, imply spectrum and RGB worth calculation, and outlier detection.
The samples for this work have been collected by a companion firm (Rester Oy) coping with the end-of-life administration of post-industrial clothes. The companion firm has data on the composition of every garment that enter their facility, obtained from the waste suppliers. The companion firm collected these clothes and the bottom fact composition data of every garment. Each garment was labelled with an identification quantity. In addition, a log guide containing metadata, together with the color and texture of the clothes, in addition to the bottom fact composition data was collected.
We obtained the clothes and the log guide. In order to accommodate samples that might match our measurement setup, we minimize an roughly 10 cm by 10 cm piece from every garment, from the placement deemed least worn out. This piece was the ultimate pattern that was utilized in our knowledge assortment.
Details of the hyperspectral digital camera setup and knowledge acquisition parameters are proven beneath. The hyperspectral photos are contained within the folder “SWIR-HSI”.
Spectral vary 1000-2500 nm
Spectral decision (FWHM): 12 nm (imply)
Spectral sampling / pixel: 5.6 nm
F/# : F/2.0
File sort: ENVI
Software: Lumo Scanner (proprietary software program by Specim)
Lighting: 6 quartz halogen lamps, directed at 45 diploma angle symmetrically on the pattern
Background materials of pattern holder: black plastic
Distance between digital camera and pattern: 30 cm
Raw sign knowledge transformed to reflectance utilizing darkish reference and white reference, gathered in tandem with pattern measurements
Dead pixels faraway from the information
Data in reflectance format
Details of the machine used for gathering red-green-blue (RGB) images of the samples are proven beneath. The images are contained within the folder “RGB”.
Device: cell phone Samsung Galaxy A52, SM-A525F/DS
ISO 50
SPEED 1/20
FOCUS MANUAL, 0.6
WB 3100K
Lighting: commonplace fluorescent lamps (Osram L58W/835)
Background materials of pattern holder: black cotton
Distance between digital camera and pattern: 26 cm
Data in RGB format
For every pattern, we obtained each NIR-HSI and RGB knowledge. In addition, with a purpose to present a single consultant knowledge level for every pattern, we calculated imply spectrum and imply RGB values. This was finished by viewing the pictures and choosing an oblong area of curiosity (ROI) of every picture. For the NIR-HSI photos, the ROI was chosen to be as giant as doable however such that solely the pixels that comprise the pattern are included. Thus, for certainty, we excluded the areas close to the perimeters of the samples. The ROIs for the pictures have been the identical for all samples and have been chosen to incorporate the most important space within the centre of all photos; this was finished with a purpose to make sure the illumination is fixed for all pattern ROIs. The ROIs for hyperspectral knowledge have been distinctive, and are contained within the file “swir_mean_spectra_coordinates.csv”.
The ROIs act as annotations and thus can be utilized to extract pixels encompassing the pattern from every picture, each NIR-HSI and RGB. An instance of that is included within the Python script. By choosing all of the pixels within the ROI, the imply spectrum and imply RGB worth of every pattern was calculated. These are contained within the information “swir_mean_spectra.csv” and “rgb_mean_values.csv”, respectively.
Here, we offer instance knowledge for a single pattern within the dataset. We chosen the pattern labelled “sample_15-1” for this. The floor fact of the pattern is proven in Table 1. A visualization of the RGB picture, the ROI, and the imply RGB worth extracted from the ROI, is proven in Fig. 2. Finally, a visualization of the NIR-HSI picture, the ROI, and the imply NIR spectrum extracted from the ROI, is proven in Fig. 3.
Ground fact of the instance pattern in tabular format.
| sample_15-1 | |
|---|---|
| Unknown? | |
| Polyamide [%] | |
| Viscose [%] | |
| Lyocell [%] | |
| Modacrylic [%] | |
| Polyacrylic [%] | |
| Wool [%] | |
| Carbon fibre [%] | |
| Polyurethane [%] | |
| Elastane [%] | |
| Polyester [%] | 100.0 |
| Cotton [%] | |
| Colour | Green |
| Texture | Fleece |
| Extra data |
Illustration of the RGB knowledge of the instance pattern: RGB picture and visualization of ROI (left), and the imply RGB worth of the ROI (proper).
Illustration of the NIR-HSI knowledge of the instance pattern: false grayscale NIR-HSI picture and visualization of ROI (left), and the imply NIR spectrum of the ROI (proper).
For outlier detection, we utilized the identical spectral pre-processing and regression modelling chain as outlined in part Example use case: Prediction of pure fibre content material within the samples. The course of was as follows: For every pattern, we obtained the expected pure fibre composition from the regression mannequin. We calculated absolutely the distinction of the expected and the bottom fact worth for all samples. Further, we calculated the usual deviation of those variations within the dataset. The samples whose distinction was higher than 3 commonplace deviations have been deemed as an outlier. The outliers have been briefly faraway from the dataset.
The course of above was repeated 3 times, with every time eradicating samples from the non permanent dataset. At the tip, 11 samples in whole have been deemed as outliers. These are marked with the label “outlier” within the floor fact metadata file (ground_truth_final.csv), into the column “Unknown or outlier?”.
In this part, we define doable use instances for the dataset. We additionally embody two instance analytics workflows utilizing machine studying for classification and regression, together with the outcomes obtained therein.
Since hyperspectral datasets for optical identification are uncommon, we anticipate a number of totally different use instances for this dataset. These are enumerated beneath. Two of those instances are included for instance on this paper (see sections Example use case: Prediction of pure fibre content material within the samples and Example use case: Classification of textiles primarily based on color).
Classification of textiles primarily based on materials composition
Classification of textiles primarily based on color (included for instance on this paper)
Prediction of pure fibre content material within the samples (included for instance on this paper)
Image segmentation primarily based on spectral data
Image segmentation primarily based on color data
Developing and optimizing machine studying algorithms for spectral classification
Developing and optimizing frameworks for spectral waveband choice for machine studying algorithms
Use as NIR reference spectral library for various supplies, e.g. utilizing the imply spectra of samples
Huge spectral library of various supplies: pixels in ROI within the hyperspectral photos
Demonstration materials for e.g. college programs on hyperspectral imaging, knowledge evaluation, and sign processing
Natural fibres within the dataset represent of cotton, wool and lyocell5. Thus, the share of those elements in every pattern was summed to kind the bottom fact for modelling. The samples labelled as unknown or outlier have been faraway from the dataset.
In this use case, we utilized the imply NIR spectra of the samples (file “swir_mean_spectra.csv”). Spectral pre-processing was finished as follows:
Data conversion from reflectance to absorbance
Savitzky-Golay filtering (2nd diploma polynomial, window measurement 9)
Cutting the spectral vary to incorporate wavelengths 1411 – 2536 nm
Standard regular variate for knowledge normalization
For regression, we utilized partial least squares. The goal worth for regression is the pure fibre composition, which was predicted utilizing the spectral knowledge. We iterated the variety of latent variables within the partial least squares mannequin within the vary 1 – 10. We utilized a leave-one-out cross-validation scheme, such that for every latent variable within the aforementioned vary, we prepare a mannequin with all however one pattern and predict the composition of the left-out pattern; that is repeated such that every pattern is not noted as soon as and thus a predicted worth is obtained for every pattern.
We obtained the very best outcomes utilizing 4 latent variables; the foundation imply squared error was 5.18% and coefficient of willpower 0.9657, which point out wonderful outcomes. A scatter plot of the prediction is proven in Fig. 4. As could be seen, a lot of the knowledge factors align properly with the very best match line. However, there was giant variation between samples. The most distinction between the expected and the precise worth was 12.4%, whereas the minimal distinction was 0.16%. These can come up from the truth that the mix share can change throughout the lifetime of the garment [6], in addition to doable errors in pattern labelling, though care was taken to take away outliers, as defined within the part Outlier detection for hyperspectral knowledge.
Cross-validation outcomes of our pure fibre content material prediction mannequin.
In order to offer robustness, we additionally carried out the identical framework outlined above however with utilizing Ok-fold cross-validation as an alternative of leave-one-out cross-validation. We utilized totally different values for Ok, specifically 3, 5, and seven. The outcomes of those approaches are present in Supplementary Material A. In quick, the outcomes utilizing Ok-fold cross-validation don’t considerably differ from these obtained with leave-one-out cross-validation, offering validity to our strategy.
We utilized the bottom fact data of the color of every pattern to construct a classification mannequin that identifies black textiles primarily based on RGB knowledge. For this, we utilized the imply RGB worth of every pattern (file “rgb_mean_values.csv”). We transformed these RGB values to CIELAB color house values. We utilized Decision Tree classifier because the classification mannequin. To gauge the efficiency of the mannequin, we used leave-one-out cross-validation scheme for the information. Note that right here, versus the evaluation within the earlier part (Example use case: Prediction of pure fibre content material within the samples), no samples have been not noted on account of being an outlier, as the bottom fact color data could possibly be verified visually.
The classification outcomes are proven in Fig. 5. We obtained 91.37% balanced accuracy, with solely 4 samples misclassified, indicating superb outcomes. Most of the misclassified samples have been false positives (3), with only one false unfavourable, i.e. the classification mannequin is barely skewed to incorrectly predict the pattern as black. The false unfavourable pattern was pattern “sample_34-1”, which based on the bottom fact data is black however “very glossy”. This almost certainly explains the faulty classification. The three false positives have been samples “sample_9-1”, “sample_25-1”, and “sample_26-1”, whose floor fact data reveals that they’re darkish colored. As such, a extra exactly calibrated algorithm may detect the distinction between these darkish colored and black samples.
Cross-validation outcomes of our color classification mannequin: classification confusion matrix.
As beforehand for the pure fibre content material prediction, we additionally carried out Ok-fold cross-validation with a purpose to take a look at the robustness of our strategy. Here, we utilized the identical values for Ok, specifically 3, 5, and seven. The outcomes of those approaches are present in Supplementary Material B. In quick, the outcomes utilizing Ok-fold cross-validation are an identical to those obtained utilizing leave-one-out cross-validation, offering validity to our strategy.
The predominant limitation of this dataset is that the bottom fact fibre composition of every garment was not verified utilizing chemical means. The samples have been acquired and labelled by our companion firm based on the knowledge supplied by the producer. It is feasible that human errors may have been made alongside the best way, or that the samples comprise unreported coatings that have an effect on the spectral response. Indeed, our outlier detection revealed that the supplied composition data of 11 samples could also be faulty. Thus, these outlier samples must be used with care. Moreover, as the bottom fact composition data is acquired from the producer primarily based on the manufacturing part, the precise fibre composition at present time could also be totally different. This is because of the truth that mix share of samples might change on account of totally different put on charges of the fibre elements, resulting in modifications in spectra as properly [6]. However, as seen in our regression mannequin outcomes (part Example use case: Prediction of pure fibre content material within the samples), we obtained superb regression outcomes, indicating that the change is just not very giant. Finally, the variety of totally different pure fibre sorts within the dataset is slightly restricted, pointing to necessities for future work.
The authors have learn the moral necessities of the journal and make sure that the present work doesn’t contain human topics, animal experiments, or any knowledge collected from social media platforms.
Please define the contributions of every co-author, utilizing the classes listed on this internetweb page.
Tuomas Sormunen: Writing – unique draft, Investigation, Software, Data curation, Methodology, Validation, Formal evaluation; Ella Mahlamäki: Investigation, Writing – evaluate & modifying, Conceptualization, Data curation; Satu-Marja Mäkelä: Conceptualization, Funding acquisition, Writing – evaluate & modifying; Mikko Mäkelä: Writing – evaluate & modifying, Funding acquisition, Methodology, Conceptualization, Supervision.
This work has been supported by the Horizon Europe undertaking tExtended: Knowledge Based Framework for Extended Textile Circulation (Grant Agreement 101091575). The authors wish to categorical gratitude to Rester Oy for offering the samples and the bottom fact composition of the samples for our work.
The authors declare that they haven’t any recognized competing monetary pursuits or private relationships that might have appeared to affect the work reported on this paper.
Strictly talking, lyocell is a semi-synthetic fibre, however is comprised of pure sources (regenerated cellulose). Thus, it’s spectral profile is much like cotton.
This part collects any knowledge citations, knowledge availability statements, or supplementary supplies included on this article.
Articles from Data in Brief are supplied right here courtesy of Elsevier
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