Comparative Analysis of Suitability of Deep Learning Models in Quality Assurance of Fabrics
DOI:
https://doi.org/10.54392/irjmt2544Keywords:
Fabric, Quality Assurance, Defect Detection, Transfer Learning, InceptionResNetV2, NasNetLarge, MobileNetV2Abstract
Industry 4.0 has revolutionized the manufacturing sector in India. The Textile Industry in India is a strong pillar of the Indian economy and leans on deploying Machine learning techniques to overcome its inherent challenges. Identifying defects in the fabric after production is a tedious process. The defect, if tiny, may not catch the attention of human vision. Fabric defect detection can be effectively done using image processing. This work analyses the capabilities of ten cutting-edge pre-trained convolutional neural networks for distinguishing between defective and non-defective fabrics, which is essential for assuring the quality of the fabric produced. For this purpose, we leverage the transfer learning models VGG16, ResNet50, InceptionV3, Xception, InceptionResNetV2, DenseNet121, NasNetLarge, EfficientNetB0, EfficientNetB3, and MobileNetV2. Fabric irregularities influence the quality of the product and consumer satisfaction. Advanced Convolutional Neural Network methods automate the detection process with reduced manual intervention, leading to standardized quality measures. We aim to determine the best-suited model for binary classification to execute the task at hand with maximum performance. This work compared improvised deep learning models by implementing them over a fabric defect dataset. This was done by fine-tuning the different models and incorporating custom layers to cater to the specific datasets. The performance of these models was evaluated using metrics such as F1-score, precision, recall, and accuracy. InceptionResNetV2 was found suitable over both defective and non-defective classes. The results of this work demonstrate the suitability of using deep learning techniques for automating fabric defect detection and, hence, the quality assurance process of fabrics.
References
Kanupriya, Linkages among trade, gender and environment: A review in the context of India’s textile sector. Decision, 51(3), (2024) 397-409. https://doi.org/10.1007/s40622-024-00397-w
W.H. Tsai, H.C. Chen, S.C. Chang, K.C. Chan, (2024). Revolutionizing Textile Manufacturing: Sustainable and Profitable Production by Integrating Industry 4.0, Activity- Based Costing, and the Theory of Constraints. Processes, 12(11), (2024) 2311. https://doi.org/10.3390/pr12112311
P. Raichurkar, M. Ramachandran, Recent trends and developments in textile industry in India. International Journal on Textile Engineering and Processes, 1(4), (2015) 47-50.
Reenu, S. Singh, S. Vig, S. Dwivedi, (2024). Impact of uses of Artificial Intelligence (AI) in Textile Industry in India. In 2024 15th Interna tional Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Kamand, India. https://doi.org/10.1109/ICCCNT61001.2024.10725995
N. Ingle, W.J. Jasper, A review of the evolution and concepts of deep learning and AI in the textile industry. Textile Research Journal, 95(13-14), (2025) 00405175241310632. https://doi.org/10.1177/00405175241310632
S. Wang, Y. Xu, H. Zheng, B. Li, High-Precision Fabric Defect Detection via Adaptive Shape Convolutions and Large Kernel Spatial Modeling. arXiv. https://doi.org/10.48550/arXiv.2501.14190
X. Li, Y. Zhu, A real-time and accurate convolutional neural network for fabric defect detection. Complex and Intelligent Systems, 10(3), (2024) 3371-3387. https://doi.org/10.1007/s40747-023-01317-8
S.S. Mohammed, H.G. Clarke, (2024) Advanced Convolutional Neural Network Approach for Fabric Defect Detection. In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE, Ankara, Turkiye. https://doi.org/10.1109/ASYU62119.2024.10757038
C. Chen, Q. Zhou, S. Li, D. Luo, G. Tan, Fabric defect detection algorithm based on improved YOLOv8. Textile Research Journal, 95(3-4), (2025) 235-251. https://doi.org/10.1177/00405175241261092
Z. Zhang, T. Huang, W. Zhang, Y. Yang, Q. Yu, DGHR-YOLO: fabric defect detection based on High-level Screening-feature Pyramid Networks and deformable convolution. Nondestructive Testing and Evaluation, (2025)1-26. https://doi.org/10.1080/10589759.2025.2451768
L. Zhou, B. Ma, Y. Dong, Z. Yin, F. Lu, DCFE-YOLO: A novel fabric defect detection method. PloS one, 20(1), (2025) e0314525. https://doi.org/10.1371/journal.pone.0314525
F. Li, K. Xiao, Z. Hu, G. Zhang, Fabric defect detection algorithm based on improved YOLOv5. The Visual Computer, 40(4), (2024) 2309-2324. https://doi.org/10.1007/s00371-023-02918-7
B. Eker, S. Erdal, (2018). Image processing technique in fabric defect control applications on textile industry. Center for Quality.
A. Beljadid, A. Tannouche, A. Balouki, Fabric defect classification using transfer learning and deep learning. International Journal of Artificial Intelligence, 12(3), (2023)1379.
K. HANBAY, Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. Türk Doğa ve Fen Dergisi, 12(4), (2023) 63-68. https://doi.org/10.46810/tdfd.1327971
M.S. Minhas, J.S. Zelek, Defect Detection using Deep Learning from Minimal Annotations. In Visigrapp, 4, (2020) 506-513.
A. Zahra, M. Amin, F.E.A. El-Samie, M. Emam, Efficient utilization of deep learning for the detection of fabric defects. Neural Computing and Applications, 36(11), (2024) 6037-6050. https://doi.org/10.1007/s00521-023-09137-0
A. Rasheed, B. Zafar, A. Rasheed, N. Ali, M. Sajid, S. Dar, U. Habib, T. Shehryar, M.T. Mahmood, Fabric defect detection using computer vision techniques: a comprehensive review. Mathematical Problems in Engineering, 2020, (2020) 1-24. https://doi.org/10.1155/2020/8189403
Z. Jia, Z. Shi, Z. Quan, M. Shunqi, Fabric defect detection based on transfer learning and improved Faster R-CNN. Journal of Engineered Fibers and Fabrics, 17, (2022) 15589250221086647. https://doi.org/10.1177/15589250221086647
X, Jun, J. Wang, J. Zhou, S. Meng, R. Pan, W. Gao, Fabric defect detection based on a deep convolutional neural network using a two- stage strategy. Textile Research Journal, 91(1-2), 130-142. https://doi.org/10.1177/0040517520935984
P. Sajjanshetti, C. Patil, M. Naik, M. Waghmare, S. Gaikwad, (2024). Automated Fabric Defect Detection (No. 12723). EasyChair.
Y. Kahraman, A. Durmuşoğlu, A. Classification of defective fabrics using capsule networks. Applied Sciences, 12(10), (2022) 5285. https://doi.org/10.3390/app12105285
Z. Chen, W.K. Wong, Z. Zhong, J. Liao, Y. Qu, (2023). Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection. arXiv. https://doi.org/10.48550/arXiv.2306.16186
S.R. Arshad, M.K. Shahzad, Deep Learning Based Fabric Defect Detection. Research Reports on Computer Science, (2024)1-11.
Y.J. Han, H.J. Yu, Fabric defect detection system using stacked convolutional denoising auto-encoders trained with synthetic defect data. Applied Sciences, 10(7), (2020) 2511. https://doi.org/10.3390/app10072511
K. Maharana, S. Mondal, B. Nemade, A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), (2022) 91-99. https://doi.org/10.1016/j.gltp.2022.04.020
C. Shorten, T.M. Khoshgoftaar, A survey on image data augmentation for deep learning. Journal of big data, 6(1), (2019) 1-48. https://doi.org/10.1186/s40537-019-0197-0
T. Kumar, R. Brennan, A. Mileo, M. Bendechache, Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access, IEEE, 12, (2024) 187536-187571. https://doi.org/10.1109/ACCESS.2024.3470122
M.A.I. Fahim, S.A. Tumpa, M.K. Newaz, Object Detection Using Machine Learning And Neural Networks.
W. Zeng, Image data augmentation techniques based on deep learning: A survey. Mathematical Biosciences and Engineering, 21(6), (2024) 6190-6224. https://doi.org/10.3934/mbe.2024272
L.B. Mahanta, D.R. Mahanta, T. Rahman, C. Chakraborty, Handloomed fabrics recognition with deep learning. Scientific Reports, 14(1), (2024) 7974. https://doi.org/10.1038/s41598-024-58750-z
A. Heikal, A. El-Ghamry, S. Elmougy, M.Z. Rashad, Fine tuning deep learning models for breast tumor classification. Scientific Reports, 14(1), (2024) 10753. https://doi.org/10.1038/s41598-024-60245-w
A.H. Barshooi, A. Yazdanijoo, E. Bagheri, A. Moosavian, (2024) Imbalanced Data Classification with Fuzzy Logic and Universal Image Fusion for Gearbox Defect Detection. In 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP) IEEE, Babol, Iran, Islamic Republic of Iran. https://doi.org/10.1109/AISP61396.2024.10475291
N. Sriraam, A. Srinivasulu, Performance evaluation of convolution neural network models for detection of abnormal and ventricular ectopic beat cardiac episodes. Multimedia Tools and Applications, 83(24), (2024) 65149-65188. https://doi.org/10.1007/s11042-023-17997-w
R.A. Saleh, M.Z. Konyar, K. Kaplan, H.M. Ertunç, End-to-end tire defect detection model based on transfer learning techniques. Neural Computing and Applications, 36(20), (2024)12483 -12503. https://doi.org/10.1007/s00521-024-09664-4
N. Akram, R.A. Butt, M. Amir, Modification of a convolutional neural network for the weave pattern classification. Mehran University Research Journal of Engineering & Technology, 43(2), (2024) 79-90. https://doi.org/10.22581/muet1982.2998
S. Lin, Z. He, L. Sun, A novel micro-defect classification system based on attention enhancement. Journal of Intelligent Manufacturing, 35(2), (2024) 703-726. https://doi.org/10.1007/s10845-022-02064-2
M. Alkanan, Y. Gulzar, Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning. Frontiers in Applied Mathematics and Statistics, 9, (2024) 1320177. https://doi.org/10.3389/fams.2023.1320177
S. Usharani, R. Gayathri, U.S.D.R. Kovvuri, M. Nivas, A.Q. Md, K.F. Tee, A.K. Sivaraman, An efficient approach for automatic crack detection using deep learning. International Journal of Structural Integrity, 15(3), (2024) 434-460. https://doi.org/10.1108/IJSI-10-2023-0102
E. Bazgir, E. Haque, M. Maniruzzaman, R. Hoque, Skin cancer classification using Inception Network. World Journal of Advanced Research and Reviews, 21(02), (2024) 839-849. https://doi.org/10.30574/wjarr.2024.21.2.0500
M.H. VARJOVİ, M.F. TALU, K. HANBAY, Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics. Türk Doğa ve Fen Dergisi, 11(3), (2022) 160-165.
B. Karsh, R.H. Laskar, R.K. Karsh, mIV3Net: modified inception V3 network for hand gesture recognition. Multimedia Tools and Applications, 83(4), (2024) 10587- 10613. https://doi.org/10.1007/s11042-023-15865-1
D. Gerdan Koc, C. Koc, H.E. Polat, A. Koc, Artificial intelligence-based camel face identification system for sustainable livestock farming. Neural Computing and Applications, 36(6), (2024) 3107-3124.
R. Sonavane, P. Ghonge, S.U. Patil, K.S. Sagale, A.A. Maha, Exploring ResNet101, InceptionV3, and Xception for Modi Script Character Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), (2024) 117-124.
A. Mohamed, M. Rabea, A. Sameh, E. Kamal, 2024). Brain Tumor Radiogenomic Classification. ArXiv.
A. Phukan, D. Gupta, Deep feature extraction from EEG signals using xception model for emotion classification. Multimedia Tools and Applications, 83(11), (2024) 33445-33463. https://doi.org/10.1007/s11042-023-16941-2
H.W. Hridoy, M.M. Rahman, S. Sakib, A framework for industrial inspection system using deep learning. Annals of Data Science, 11(2),(2024) 445-478. https://doi.org/10.1007/s40745-022-00437-1
W.K. ElHelew, D. Abo-Bbakr, S. Zayan, M.A. Mayhoub, Classification of Dates Quality Using Deep Learning Technology Based On Captured Images. MisrJournal of Agricultural Engineering.41(3), (2024) 225-242. https://doi.org/10.21608/mjae.2024.286079.1137
J. Rashid, B.S. Qaisar, M. Faheem, A. Akram, R.U. Amin, M. Hamid, Mouth and oral disease classification using InceptionResNetV2 method. Multimedia Tools and Applications, 83(11), (2024) 33903-33921. https://doi.org/10.1007/s11042-023-16776-x
A. Sanampudi, S. Srinivasan, Local search enhanced optimal Inception- ResNet- v2 for classification of long-term lung diseases in post-COVID-19 patients. Automatika, 65(2), (2024) 473-482. https://doi.org/10.1080/00051144.2023.2295142
N.R. Pradhan, H. Ghosh, I.S. Rahat, J.V.N. Ramesh, M. Yesubabu, (2024). Enhancing Agricultural Sustainability with Deep Learning: A Case Study of Cauliflower Disease Classification. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.4834
A. Sanampudi, S. Srinivasan, Local search enhanced optimal Inception- ResNet-V2 for classification of long-term lung diseases in post-COVID-19 patients. Automatika, 65(2), (2024) 473-482. https://doi.org/10.1080/00051144.2023.2295142
H.P. Hadi, E.H. Rachmawanto, R.R. Ali, Comparison of DenseNet-121and MobileNet for Coral Reef Classification. MATRIK: Jurnal Manajemen, TeknikInformatika dan Rekayasa Komputer, 23(2), (2024) 333-342. https://doi.org/10.30812/matrik.v23i2.3683
K. An, X. Sun, Y. Song, Y. Lu, Q. Shangguan, (2024). A DenseNet◻based feature weighting convolutional network recognition model and its application in industrial part classification. IET Image Processing, 18(3), (2024) 589-601. https://doi.org/10.1049/ipr2.12971
C.A.D. Lestari, S. Anam, U. Sa’adah, Tomato Leaf Disease Classification with Optimized Hyperparameter: A DenseNet-PSO Approach. In First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)), Atlantis Press. 228-239. https://doi.org/10.2991/978-94-6463-413-6_23
D. Wang, Y. Xia, Z. Yu, (2024). Feature-based Interpretation of Image Classification with the use of Convolutional Neural Networks. IEEE Access,IEEE. https://doi.org/10.1109/ACCESS.2024.3397871
P. Gifani, A. Shalbaf, (2024). Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays. Journal of Medical Signals & Sensors, 14(1), (2024) 4.
R. Angeline, A.A. Nithya, Deep Human Facial Emotion Recognition: a transfer learning approach using efficientnetb0 model. Journal of Theoretical and Applied Information Technology, 102(8), (2024).
Q.T. Pham, D.D. Pham, K.L. Can, H.D. To, H. D. Vu,Vehicle Type Classification with Small Dataset and Transfer Learning Techniques. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 11(2), (2024) e2-e2.
M.A. Talukder, M.A. Layek, M. Kazi, M.A. Uddin, S. Aryal, Empowering covid-19 detection: Optimizing performance through fine-tuned efficient net deep learning architecture. Computers in Biology and Medicine, 168, (2024)107789. https://doi.org/10.1016/j.compbiomed.2023.107789
P. Shourie, V. Anand, D. Upadhyay, V. Singh, S. Gupta, (2024) Leveraging the capabilities of efficientnetb3 to classify eye diseases with reliability. In 2024 Fourth International Conference on Advances in Electrical, Computing,Communication and Sustainable Technologies (ICAECT),IEEE, 1-5. http://dx.doi.org/10.1109/ICAECT60202.2024.10469017
A. Kaur, V. Kukreja, N. Thapliyal, M. Aeri, R. Sharma, S. Hariharan, (2024) Pre-trained VGG16 and EfficientNetB3 for Multiclass Classification of Orange Leaf Diseases. In 2024 3rd International Conference for Innovation in Technology (INOCON), IEEE, Bangalore, India.1-6. https://doi.org/10.1109/INOCON60754.2024.10511701
M.S. Chandrasekar, M. Thenmozhi, R. Sandya Sree, N. Pradeepika, Multi Plant Disease and Classification with Enhanced Efficientnet B3 Using Deep Learning Techniques.
J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Textile Research Journal, 92(1-2), (2022) 30-42.
K. Wu, L. Zhu, W. Shi, W. Wang, Automated fabric defect detection using multi-scale fusion MemAE. The Visual Computer, 41, (2025)723-737. https://doi.org/10.1007/s00371-024-03358-7
T. Meeradevi, S. Sasikala, (2024). Automatic fabric defect detection in textile images using a labview based multiclass classification approach. Multimedia Tools and Applications, 83(25), (2024) 65753-65772. https://doi.org/10.1007/s11042-023-18087-7
M. Kulkarni, P.C. Cholke, P. Vasmatkar, S. Tambe, S. Anuse, S. Telgote, (2024) Fabric Defect Detection System Using Image Processing. In 2024 1st International Conference on Cognitive. Green and Ubiquitous Computing, IEEE, 1-7. https://doi.org/10.1109/ic-cgu58078.2024.10530850
P. Yashini, G. Karthika, T. Sunitha, R. Berlin Magthalin, R. (2024) Machine Learning-Based Textile Fabric Defect Detection Network. In 4th International Conference on Sustainable Expert Systems, IEEE, Kaski, Nepal, 1470-1477. https://doi.org/10.1109/ICSES63445.2024.10763088
N. Sajitha, S.P. Priya, Optimal Artificial Neural Network-based Fabric Defect Detection and Classification. Engineering, Technology & Applied Science Research, 14(2), (2024)13148-13152. https://doi.org/10.48084/etasr.6773
Q. Liu, C. Wang, Y. Li, M. Gao, J. Li, A fabric defect detection method based on deep learning. IEEE access, 10, (2022) 4284-4296. https://doi.org/10.1109/ACCESS.2021.3140118
A. Beljadid, A. Tannouche, A. Balouki, (2022). Automatic fabric defect detection employing deep learning. International Journal of Electrical and Computer Engineering, 12(4), (2022) 4129. http://doi.org/10.11591/ijece.v12i4.pp4129-4136
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Malathi S, Aiswarya P.U (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
