R03 · Grade C · inherited record
SODECOTON
Inherited evidence R03
Company profile: more than 220,000 cotton producers supported in northern Cameroon; nine ginning plants; two oil mills.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R09 · Grade C · inherited record
Ecofin Agency
Inherited evidence R09
Camtext SA integrated cotton-to-textile project, US$300 million (180 billion XAF); Panafritex and Arise with SODECOTON, CNPS and Marlo; 12 November 2025.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R10 · Grade C · inherited record
Ecofin Agency
Inherited evidence R10
Camtext SA: 12,000 t a year processed; about 12,000 jobs at the Dibamba hub and 3,000 at Garoua; return on investment near 11 years; optimal capacity around 2033.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R14 · Grade C · inherited record
WageIndicator
Inherited evidence R14
Cameroon minimum wage (SMIG) XAF 41,875 a month in 2023, raised to XAF 43,969 a month from 23 February 2024, about US$76, a rise of 5 percent.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
R29 · Grade C · inherited record
MDPI; industry inspection literature
Inherited evidence R29
Manual defect inspection 60 to 70 percent accurate, missing 20 to 30 percent of defects; computer vision above 90 percent across more than 40 defect types; line-scan inspection up to 60 metres a minute.
Publication date unrecorded. Inherited source-text record. The original publication has not been independently reopened for this web edition.
c04-S02 · Grade B
Sensors (MDPI)
Deep Convolutional Neural Network Optimization for Defect Detection in Fabric Inspection
Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively.
2021-10-25. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S06 · Grade B
arXiv / Computer Science and Information Technology
One-Class Model for Fabric Defect Detection
Our model also achieves a true positive rate (a.k.a recall) value of 0.895 with no false alarms on our dataset based upon the Standard Fabric Defect Glossary.
2022-04-20. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S07 · Grade B
arXiv
Low Cost Two-Stage Fabric Defect Detection at the Edge
Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small and medium mills.
2026-08-12. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S12 · Grade B
arXiv
Distillation-based fabric anomaly detection
To demonstrate the capabilities of our approach both in terms of performance and inference speed, we conducted a series of experiments on multiple texture datasets, including MVTEC AD, AITEX, and TILDA, alongside conducting experiments on a dataset acquired from a textile manufacturing facility.
2024-01-04. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S14 · Grade B
Applied Sciences (MDPI)
Design and Development of a Precision Defect Detection System Based on a Line Scan Camera Using Deep Learning
Small and medium-sized manufacturing companies are increasingly demanding smart factory solutions for quality control to create added value and enhance competitiveness. In this paper, we design and develop a high-speed defect detection system based on a line-scan camera using deep learning.
2024-12-23. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S15 · Grade B
Sensors (MDPI)
Online Fabric Defect Inspection Using Smart Visual Sensors
The proposed system consists of multiple smart visual sensors and a controller. Each sensor can scan 800 mm width of web, and can work independently.
2013-04-09. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S16 · Grade B
Textile Research Journal
DF-YOLO: An attempt on enhancing generalization in fabric defect detection based on YOLO network
Utilizing Fourier transform, the network better discerns fabric textures and anomalies, addressing traditional convolutional neural networks' limited receptive field issue.
2024-09-24. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S17 · Grade B
Journal of Measurements in Engineering
Improved YOLOv8 garment sewing defect detection method based on attention mechanism
The CA attention mechanism emphasizes the importance of location information, and the model has better accuracy and recall after adding the CA attention mechanism, with a 3.7 % increase in mAP@0.5, indicating that YOLOv8-CA has better performance in sewing defect detection.
2024-09-09. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S18 · Grade B
International Journal of Electrical and Electronics Research
Deep-GD: Deep Learning based Automatic Garment Defect Detection and Type Classification
Finally, the proposed Deep-GD model is used for classifying defected fabric into 12 classes such as Defect free, soil stain, oil stain, Double end, Snarls, Miss, Horizontal stripes, Lumpy, Dye spot, Fall out, Hairiness, tiny hole. The proposed Deep-GD model achieves the overall classification accuracy of 97.16%, which is comparatively superior to the existing techniques.
2024-02-05. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S19 · Grade B
Textile Research Journal
Development of an automatic garment defect recognition system using a machine learning approach
The system processes images in 0.45 seconds per sample on consumer-grade hardware, representing a substantial 60-fold improvement over manual inspection throughput while maintaining consistent defect detection criteria.
2026-09-06. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S20 · Grade B
International Journal of e-Collaboration
Intelligent Detection Algorithm for Garment Pattern Defects Based on Computer Vision and Deep Learning
An experimental analysis of an open-source garment stitching defect dataset shows that IDAGPD-DL achieves 96.3% detection accuracy, 95.1% precision, 94.6% recall, and 94.8% F1-score, which is better than current baseline methods by about 7-10 points across all performance metrics.
2026-10-26. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S21 · Grade B
Textile Research Journal
Image analysis for seam-puckering evaluation
Until now, the standardized evaluation method for seam-puckering grading is still a visual-based, subjective method. This research project was aimed at developing a computer-vision system for automatic seam-puckering evaluation to improve the consistency and efficiency of grading.
2016-10-14. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S22 · Grade B
arXiv
Defect Detection Techniques for Airbag Production Sewing Stages
Types of defects as skipped stitch, missed stitch or superimposed seam for lockstitch and two threads chainstitch are detected and marked. Using image processing methods, the proposed framework follows the seams path and determines if a color pattern of the considered stitches is valid.
2015-10-25. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S23 · Grade B
University of North Texas Libraries
On-Loom Fabric Defect Inspection Using Contact Image Sensors and Activation Layer Embedded Convolutional Neural Network
The novel CIS-based on-loom scanning system offered real-time and high-resolution fabric images, which was able to deliver the information of single thread on a fabric. The algorithm evaluation on the fabric defect datasets showed a non-miss-detection rate on defect-free fabrics. The average precision of defect existed images reached above 90% at the pixel level.
2024-05-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S25 · Grade B
OPSI
Quality control of garment product using DMAIC Six Sigma
Based on Pareto diagram at the Analyze stage, it was found that the most dominant defects occurred in the Just Brand-MCJA216142 jacket production process were broken threads and puckering. The frequency of occurrence for these two types of defect reaches 23% of the total 16 types of defects.
2024-06-30. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S26 · Grade B
Jurnal Teknik Industri (JATRI)
Analysis of Quality Control and Product Failure Risks in Garment Manufacturing Using Statistical Quality Control (SQC) and Failure Mode and Effect Analysis (FMEA) Case Study: CV Muharram Garment Indonesia
The results showed that out of 1,380 produced units, 107 units were defective, consisting of 43 faded-print defects, 36 misaligned-print defects, and 28 inappropriate-color defects.
2026-09-25. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0401 · Grade B
Production Systems and Information Engineering
Search algorithm for determining the illumination frequency in a line scan camera-based measurement system
A critical challenge in such systems is synchronizing the camera's exposure frequency with the lighting control pulse train. Discrepancies between these two frequencies could lead to periodic intensity variations in the image.
2025-12-09. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0402 · Grade B
arXiv
Fabric Defect Detection Using Vision-Based Tactile Sensor
The tactile sensor, which mainly consists of a camera, four LEDs, and an elastic sensing layer, captures detailed information about fabric surface structure and ignores the color and pattern.
2020-03-02. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0403 · Grade B
Electronics (MDPI)
LUSITANOv2: A Real-World Dataset for Fabric Defect Detection in Active Textile Production
This paper introduces LUSITANOv2, a high-resolution fabric-defect dataset collected at an active textile inspection station with an industrial line-scan camera and directional illumination. It contains 25,120 native images, including both defect-containing and defect-free fabric, together with 18,557 class-agnostic bounding boxes.
2026-09-24. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0404 · Grade B
Scientific Reports (Nature Portfolio)
Novel online fabric color difference detection system based on machine vision
The proposed system integrates a custom-designed optical acquisition platform with a lightweight color analysis pipeline, including bilateral filtering for noise suppression, K-means clustering for representative color extraction, RGB-to-CIELab color space conversion, and perceptually weighted computation.
2026-06-22. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0405 · Grade B
International Journal Science and Technology
Industrial Artificial Intelligence Methods for Fabric Color-Defect Detection in Textile Manufacturing
Color-related fabric defects such as shade variation, off-shade, discoloration, and color bleeding remain challenging to detect consistently in textile manufacturing because camera-based inspection is highly influenced by illumination variability, fabric reflectance, and domain shift across production batches.
2026-03-01. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗c04-S0406 · Grade B
Data (MDPI)
A Dataset of Raw Fabric Grayscale Images for Defect Detection
It comprises 709 high-resolution images (1792 x 1024 pixels), including both defect-free and defective samples. To maximize reusability, data are provided in two complementary formats: high-resolution images (cropped to remove peripheral acquisition artifacts) for global analysis, and a patch-based organization following the widely adopted MVTec Anomaly Detection benchmark structure.
2026-05-12. Register access date: 2026-10-30. Source provenance is supplied by the paper evidence register.
Open the recorded source ↗