Characterization of near infrared dye coloured fabrics using hyperspectral imaging
Bibliographic record
Abstract
The near-infrared (NIR) region and the visible spectrum play a pivotal role in military applications by enabling effective camouflage against adversaries in these regions. This study applied NIR-absorption dyes to cotton and nylon fabrics, the predominant fibre materials used in military battledress uniforms (BDUs). The primary objectives were to investigate the underlying fibre responsible for the suboptimal performance of military gear when dyed with NIR-absorption dyes and to assess the efficacy of visible near-infrared (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) techniques in detecting shade changes in cotton fabric dyed with NIR-absorbing dyes. Variations in dyeing parameters cause undesirable changes in fabric prints, which can adversely impact the camouflage effect of military uniforms, compromising personnel safety. The fabrics were dyed using various concentrations and temperatures of dyes. Both cotton and nylon materials underwent rigorous testing for lightfastness and staining resistance under wet and dry conditions. The experimental results demonstrate that coloured cotton fabric exhibited superior lightfastness and staining resistance performance compared to nylon. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to analyze the extent of shade differences that can be detected by measuring reflectance in the Vis-NIR and SWIR regions. These results establish that differently dyed fabrics can be seen well in the Vis-NIR spectral region, which implies the importance of selecting optimal parameters such as dye concentration and dyeing temperature to produce colours that match the background spectra. Also, all dyed samples (irrespective of colour, concentration, and dyeing temperature) gave similar signatures in the SWIR region, making segregating them difficult. Thus, the experimental design variations do not affect the SWIR region, warranting further investigation. The established classification models find practical utility in modelling spectral variations under various dyeing conditions, particularly in the NIR region, a critical consideration for applications such as military uniform development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".