A new system uses cameras and computer vision to automatically detect defects and improve quality in indigo dyeing machines.
The system consists of two parts: one detects yarn defects with high accuracy, and the other monitors color and recommends adjustments to maintain quality. Experimental results showed a 92.4% detection accuracy, improved production speed, and reduced operator workload.
Abstract
This paper presents the development of a multi-step, multi-disciplinary automation framework designed to enhance quality assurance and process control in slasher indigo dyeing machines. The system integrates two complementary subsystems: (1) a real-time yarn defect detection module employing deep learning-based computer vision, and (2) a process optimization module utilizing chromaticity analysis for colour stability and chemical balance control. The defect detection system uses four moving cameras strategically placed across the machine to identify broken yarns and irregular density patterns with high accuracy. The colour monitoring subsystem, developed in collaboration with Agteks, continuously records yarn colour in the CIELAB colour space and recommends corrective pH or reduction agent (Hydro) adjustments when deviations occur. Experimental results demonstrate a detection accuracy of 92.4%, with significant improvements in production speed, consistency, and operator workload reduction. The proposed system represents a comprehensive step toward fully autonomous dyeing operations aligned with Industry 4.0 objectives.
References
- 1.M. Solli, M. Andersson, R. Lenz, and B. Kruse, “Color Measurements with a Consumer Digital Camera Using Spectral Estimation Techniques,” Proceedings of SPIE – Color Imaging: Device-Independent Color, Color Hardcopy, and Applications X, vol. 5667, pp. 253–263, 2005.
- 2.J. Zhang, J. Wu, X. Hu, and X. Zhang, “Multi-Color Measurement of Printed Fabric Using the Hyperspectral Imaging System,” Journal of Imaging Science and Technology, vol. 63, no. 5, 2019.
- 3.Q. C. Wang, J. F. Jing, L. Zhang, X. H. Wang, and P. F. Li, “Denim Defect Detection Based on Optimal Gabor Filter,” Textile Research Journal, vol. 88, no. 16, pp. 1827–1836, 2018.
- 4.H. I. Celik, M. Topalbekiroglu, and L. C. Dulge, “Real-Time Denim Fabric Inspection Using Image Analysis,” Textile Research Journal, vol. 85, no. 18, pp. 1905–1916, 2015.
- 5.M. F. Talu, K. Hanbay, and M. H. Varjovi, “CNN-Based Fabric Defect Detection System on Loom Fabric Inspection,” Sensors, vol. 22, no. 15, p. 5668, 2022.
- 6.M. S. Gu, J. Zhou, R. Pan, and W. Gao, “Unsupervised Defect Segmentation on Denim Fabric via Local Patch Prediction and Residual Fusion,” IEEE Access, vol. 11, pp. 25492–25504, 2023.
- 7.Daheng Imaging, “MER2-160-227U3C MER2-U3 Series” Daheng Imaging Official Website. [Online]. Available: https://en.daheng-imaging.com/show-106-1969-1.html [Accessed: Nov. 11, 2025].Link
- 8.M. Muttaqi, A. Degirmenci, and O. Karal, “US Accent Recognition Using Machine Learning Methods,” 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–6, Sep. 2022.