HU MOMENTLERI İLE GÖRÜNTÜ İŞLEME TABANLI GÖMLEK BOYUT TESPITI
Öz
This study proposes a hybrid classification model for the automatic detection of shirt sizes in textile quality control processes. The model integrates Hu moment-based numerical features with a convolutional neural network (CNN) architecture, combining the interpretability of traditional image processing with the classification performance of deep learning methods. The proposed CNN+Hu moments approach was trained and tested on a controlled dataset comprising various shirt size categories (XXS, XS, S, M, L, XL). Experimental results demonstrate that the hybrid model significantly outperforms classical machine learning algorithms such as Support Vector Machines (SVM) and KDTree in terms of accuracy and robustness. With its low cost, explainability, and effectiveness, this approach offers a new dimension to quality control systems in the textile industry.
Anahtar Kelimeler
Hu moments shirt size detection quality control KDTree classification.
Kaynakça
- Prabha, K., A., K, H., & K, I. (2024). Automated Defect Detection and Segregation in Dyed Fabrics Using Image Processing Techniques. 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 974-979. https://doi.org/10.1109/icesc60852.2024.10689823
- Zhang, C., Feng, S., Wang, X., & Wang, Y. (2020). ZJU-Leaper: A Benchmark Dataset for Fabric Defect Detection and a Comparative Study. IEEE Transactions on Artificial Intelligence, 1, 219-232. https://doi.org/10.1109/tai.2021.3057027
- Nguyen, T., Nguyen, H., & Ngo, H. (2023). Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems. Textile Research Journal, 93, 5391-5415. https://doi.org/10.1177/00405175231199257
- Sundaram, S., & Zeid, A. (2023). Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines, 14. https://doi.org/10.3390/mi14030570
- Kumar, S., Muthuvelammai, S., & Jayachandran, N. (2024). AI in Textiles: A Review of Emerging Trends and Applications. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2024.64404
- Tu, Y., Kwan, M., & Yick, K. (2024). A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Materials, 17. https://doi.org/10.3390/ma17205009
- Muzaffar, A., Riaz, F., Abuain, T., Abu-Ain, W., Hussain, F., Farooq, M., & Azad, M. (2023). Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images. IEEE Access, 11, 60324-60334. https://doi.org/10.1109/access.2023.3280053
- Hazgui, M., Ghazouani, H., & Barhoumi, W. (2021). Evolutionary-based generation of rotation and scale invariant texture descriptors from SIFT keypoints. Evolving Systems, 12, 591-603. https://doi.org/10.1007/s12530-021-09386-1
- Ribas, L., & Bruno, O. (2024). Learning a complex network representation for shape classification. Pattern Recognit., 154, 110566. https://doi.org/10.1016/j.patcog.2024.110566
- Yang, C., Fang, L., Fei, B., Yu, Q., & Wei, H. (2023). Multi-level contour combination features for shape recognition. Comput. Vis. Image Underst., 229, 103650. https://doi.org/10.1016/j.cviu.2023.103650
- Wang, K., Yin, Y., Du, S., & Xi, L. (2021). Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium. Journal of Manufacturing Systems, 59, 441-452. https://doi.org/10.1016/j.jmsy.2021.03.013
- Hu, M. K. (1961). Visual pattern recognition by moment invariants. Proceedings of the IRE, 49, 1428-1431. https://doi.org/10.1109/tit.1962.1057692
- Singh, B., Rai, A., Kundu, K., Kalita, K., & Agrawal, R. (2024). An Empirical Analysis of Invariance Hu’s Moment Feature over a Digital Image. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1-5. https://doi.org/10.1109/icrito61523.2024.10522255
- Rodríguez, J. (2023). Micro-Scale Surface Recognition via Microscope System Based on Hu Moments Pattern and Micro Laser Line Projection. Metals. https://doi.org/10.3390/met13050889
- Wang, J., Lin, S., & Zhang, K. (2024). An Edge Detection Algorithm of Noisy Image Based on OTSU Adaptive Threshold Segmentation. 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 547-551. https://doi.org/10.1109/ipec61310.2024.00099
- Fan, Y., Wei, L., Li, L., Yang, L., Hu, Z., Zheng, Y., & Wang, Y. (2023). Research on the Modulation Transfer Function Detection Method of a Bayer Filter Color Camera. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23094446
- Islam, N., Fatema-Tuj-Jahra, M., Hasan, M., & Farid, D. (2023). KNNTree: A New Method to Ameliorate K-Nearest Neighbour Classification using Decision Tree. 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1-6. https://doi.org/10.1109/ecce57851.2023.10101569
- Mohalder, R., Hossain, M., & Hossain, N. (2024). Classifying the Supervised Machine Learning and Comparing the Performances of the Algorithms. International Journal of Advanced Research. https://doi.org/10.21474/ijar01/18138
- Nguyen, M. (2024). Establishing the program to predict Shirt Sizes with Fuzzy Logic. International Journal of Current Science Research and Review. https://doi.org/10.47191/ijcsrr/v7-i9-41
- Nugroho, E., Mulyadi, D., & Wibowo, N. (2024). Sistem Klasifikasi Citra untuk Proses Inspeksi Kain Menggunakan Teachable Machine dan Raspberry Pi. Jurnal Teknologika. https://doi.org/10.51132/teknologika.v14i1.368
HU MOMENTLERI İLE GÖRÜNTÜ İŞLEME TABANLI GÖMLEK BOYUT TESPITI
Öz
Bu çalışma, tekstil sektöründeki kalite kontrol süreçlerinde, gömlek boyutlarının otomatik olarak tespiti için hibrit bir sınıflandırma modeli önermektedir. Model, Hu moment tabanlı sayısal özelliklerin evrişimsel sinir ağı (CNN) yapısıyla bütünleştirilmesiyle oluşturulmuştur. Bu sayede hem geleneksel görüntü işleme temelli açıklanabilirlik sağlanmış hem de derin öğrenme yöntemlerinin sınıflandırma başarımı elde edilmiştir. Geliştirilen model, kontrollü bir veri kümesinde uygulanarak farklı beden grupları (XXS, XS, S, M, L, XL) üzerinde test edilmiştir. Elde edilen sonuçlar, önerilen CNN+Hu moment hibrit yaklaşımının, klasik makine öğrenmesi algoritmalarına (SVM, KDTree) kıyasla daha yüksek doğruluk ve kararlılık sunduğunu göstermektedir. Bu yönüyle çalışma, kalite kontrol süreçlerine yeni bir boyut kazandırmakta ve tekstil sektörüne yönelik düşük maliyetli, açıklanabilir ve etkin bir çözüm sunmaktadır.
Anahtar Kelimeler
Hu momentleri gömlek boyutu tespiti kalite kontrol KDTree sınıflandırma
Kaynakça
- Prabha, K., A., K, H., & K, I. (2024). Automated Defect Detection and Segregation in Dyed Fabrics Using Image Processing Techniques. 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 974-979. https://doi.org/10.1109/icesc60852.2024.10689823
- Zhang, C., Feng, S., Wang, X., & Wang, Y. (2020). ZJU-Leaper: A Benchmark Dataset for Fabric Defect Detection and a Comparative Study. IEEE Transactions on Artificial Intelligence, 1, 219-232. https://doi.org/10.1109/tai.2021.3057027
- Nguyen, T., Nguyen, H., & Ngo, H. (2023). Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems. Textile Research Journal, 93, 5391-5415. https://doi.org/10.1177/00405175231199257
- Sundaram, S., & Zeid, A. (2023). Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines, 14. https://doi.org/10.3390/mi14030570
- Kumar, S., Muthuvelammai, S., & Jayachandran, N. (2024). AI in Textiles: A Review of Emerging Trends and Applications. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2024.64404
- Tu, Y., Kwan, M., & Yick, K. (2024). A Systematic Review of AI-Driven Prediction of Fabric Properties and Handfeel. Materials, 17. https://doi.org/10.3390/ma17205009
- Muzaffar, A., Riaz, F., Abuain, T., Abu-Ain, W., Hussain, F., Farooq, M., & Azad, M. (2023). Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images. IEEE Access, 11, 60324-60334. https://doi.org/10.1109/access.2023.3280053
- Hazgui, M., Ghazouani, H., & Barhoumi, W. (2021). Evolutionary-based generation of rotation and scale invariant texture descriptors from SIFT keypoints. Evolving Systems, 12, 591-603. https://doi.org/10.1007/s12530-021-09386-1
- Ribas, L., & Bruno, O. (2024). Learning a complex network representation for shape classification. Pattern Recognit., 154, 110566. https://doi.org/10.1016/j.patcog.2024.110566
- Yang, C., Fang, L., Fei, B., Yu, Q., & Wei, H. (2023). Multi-level contour combination features for shape recognition. Comput. Vis. Image Underst., 229, 103650. https://doi.org/10.1016/j.cviu.2023.103650
- Wang, K., Yin, Y., Du, S., & Xi, L. (2021). Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium. Journal of Manufacturing Systems, 59, 441-452. https://doi.org/10.1016/j.jmsy.2021.03.013
- Hu, M. K. (1961). Visual pattern recognition by moment invariants. Proceedings of the IRE, 49, 1428-1431. https://doi.org/10.1109/tit.1962.1057692
- Singh, B., Rai, A., Kundu, K., Kalita, K., & Agrawal, R. (2024). An Empirical Analysis of Invariance Hu’s Moment Feature over a Digital Image. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1-5. https://doi.org/10.1109/icrito61523.2024.10522255
- Rodríguez, J. (2023). Micro-Scale Surface Recognition via Microscope System Based on Hu Moments Pattern and Micro Laser Line Projection. Metals. https://doi.org/10.3390/met13050889
- Wang, J., Lin, S., & Zhang, K. (2024). An Edge Detection Algorithm of Noisy Image Based on OTSU Adaptive Threshold Segmentation. 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 547-551. https://doi.org/10.1109/ipec61310.2024.00099
- Fan, Y., Wei, L., Li, L., Yang, L., Hu, Z., Zheng, Y., & Wang, Y. (2023). Research on the Modulation Transfer Function Detection Method of a Bayer Filter Color Camera. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23094446
- Islam, N., Fatema-Tuj-Jahra, M., Hasan, M., & Farid, D. (2023). KNNTree: A New Method to Ameliorate K-Nearest Neighbour Classification using Decision Tree. 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1-6. https://doi.org/10.1109/ecce57851.2023.10101569
- Mohalder, R., Hossain, M., & Hossain, N. (2024). Classifying the Supervised Machine Learning and Comparing the Performances of the Algorithms. International Journal of Advanced Research. https://doi.org/10.21474/ijar01/18138
- Nguyen, M. (2024). Establishing the program to predict Shirt Sizes with Fuzzy Logic. International Journal of Current Science Research and Review. https://doi.org/10.47191/ijcsrr/v7-i9-41
- Nugroho, E., Mulyadi, D., & Wibowo, N. (2024). Sistem Klasifikasi Citra untuk Proses Inspeksi Kain Menggunakan Teachable Machine dan Raspberry Pi. Jurnal Teknologika. https://doi.org/10.51132/teknologika.v14i1.368
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Görüntü İşleme |
| Bölüm | Bilgisayar Mühendisliği |
| Yazarlar | |
| Yayımlanma Tarihi | 3 Eylül 2025 |
| Gönderilme Tarihi | 22 Nisan 2025 |
| Kabul Tarihi | 16 Temmuz 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 28 Sayı: 3 |
