COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION

Yasin Ulus; Mehmet Akif Şahman

TR EN

SOMUN ÜRETİMİNDE YÜZEY KUSURLARININ TESPİTİ İÇİN GÖRÜNTÜ İŞLEME VE CNN'LERİN KARŞILAŞTIRMALI ÇALIŞMASI

Abstract

Üretimde, özellikle endüstride, ürünlerin hizmet ömrünü uzatmak ve birçok bileşenden oluşan sistemlerin güvenliğini en üst düzeyde tutmak büyük önem taşımaktadır. Ürünler doğru yöntem ve teknikler kullanılarak analiz edilmelidir. Bu analizler, özellikle metal ve malzeme üretiminde farklı teknik ve prosedürler kullanılarak gerçekleştirilir. Manyetik Parçacık Testi (MPT), ferromanyetik malzemelerin tahribatsız muayenesi için kullanılan bir testtir. Ancak burada kusur tespiti insan gözüyle yapılan görsel inceleme yoluyla gerçekleştirilir. Bu durum, tespit sürecinin doğruluğunu ve eksiksizliğini olumsuz etkilemektedir. Bu çalışmada, endüstriyel alanda yaygın olarak kullanılan kritik bileşenlerden biri olan somunların yüzeyinde veya yüzeyine yakın yerlerde meydana gelebilecek kusurları tespit etmek için yapay zekâ modellerini çalıştırabilen bir kamera ve özel olarak üretilmiş bir kit kullanılarak bir sistem geliştirilmiştir. Sistemle iki sınıf için dengeli bir veri kümesi oluşturulmuştur. Bu veri kümesi, farklı alanlarda uygulanan ve başarılı sonuçlar elde eden derin öğrenme tabanlı modeller kullanılarak eğitilmiştir. Elde edilen test sonuçları farklı metrikler kullanılarak değerlendirilmiş ve karşılaştırılmıştır. Değerlendirmelerde, VGG-16 yaklaşımı %89,33 doğruluk oranıyla en iyi sonucu elde etmiştir.

Keywords

COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION

Abstract

In production, especially in industry, it is of great importance to increase the service life of products and maintain the safety of systems consisting of many components at the maximum level. Products must be analyzed using the correct methods and techniques. These analyses are performed using different techniques and procedures, particularly in metal and material production. Magnetic Particle Testing (MPT) is a test used for non-destructive inspection of ferromagnetic materials. However, defect detection here is performed through human visual inspection. This situation negatively affects the accuracy and completeness of the detection process. In this study, a system has been developed using a camera and a specially developed kit capable of running artificial intelligence models to detect defects that may occur on the surface or near the surface of nuts, one of the critical components widely used in the industrial field. Using this system, a balanced dataset consisting of two classes was created. This dataset is trained using deep learning-based models that are currently applied in different fields and have achieved successful results. The test results obtained are evaluated and compared using different metrics. In the evaluations, the VGG-16 approach achieved the best result with an accuracy rate of 89.33%.

Keywords

Project Number

The authors gratefully acknowledge the financial support from the Scientific Research Projects Coordination Unit of Selçuk University (Project No: 24201044)

References

Binas, J., Rutishauser, U., Indiveri, G., & Pfeiffer, M. (2014). Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity. Frontiers in computational neuroscience, 8, 68. https://doi.org/10.3389/fncom.2014.00068
Boursalie, O., Samavi, R., & Doyle, T. E. (2021). Evaluation metrics for deep learning imputation models. Paper presented at the International Workshop on Health Intelligence. https://doi.org/10.1007/978-3-030-93080-6_22
Dwivedi, S. K., Vishwakarma, M., & Soni, A. (2018). Advances and researches on non destructive testing: A review. Materials Today: Proceedings, 5(2), 3690-3698. https://doi.org/10.1016/j.matpr.2017.11.620
Fan, Z., Li, C., Chen, Y., Di Mascio, P., Chen, X., Zhu, G., & Loprencipe, G. (2020). Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. Coatings, 10(2), 152. https://doi.org/10.3390/coatings10020152
Fernandez, L., Castillero, C., & Aguilera, J. (2005). An application of image analysis to dehydration of apple discs. Journal of food engineering, 67(1-2), 185-193. https://doi.org/10.1016/j.jfoodeng.2004.05.070
Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M. Y., & Cao, Y. (2019). A deep-learning-based approach for fast and robust steel surface defects classification. Optics and Lasers in Engineering, 121, 397-405. https://doi.org/10.1016/j.optlaseng.2019.05.005
Gehri, N., Mata-Falcón, J., & Kaufmann, W. (2023). Experimental investigation of the shear response of large-scale fibre-reinforced concrete panels. Engineering Structures, 295, 116598. https://doi.org/10.1016/j.engstruct.2023.116598
Gholizadeh, S. (2016). A review of non-destructive testing methods of composite materials. Procedia structural integrity, 1, 50-57. https://doi.org/10.1016/j.prostr.2016.02.008

Golding, V. P., Gharineiat, Z., Munawar, H. S., & Ullah, F. (2022). Crack detection in concrete structures using deep learning. Sustainability, 14(13), 8117. https://doi.org/10.3390/su14138117
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. https://doi.org/10.1145/3422622
Hao, X., Zhang, G., & Ma, S. (2016). Deep learning. International Journal of Semantic Computing, 10(03), 417-439. https://doi.org/10.1142/S1793351X16500045
Hatcher, D., Symons, S., & Manivannan, U. (2004). Developments in the use of image analysis for the assessment of oriental noodle appearance and colour. Journal of food engineering, 61(1), 109-117. https://doi.org/10.1016/S0260-8774(03)00192-4
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2016.90
Helal, J., Sofi, M., & Mendis, P. (2015). Non-destructive testing of concrete: A review of methods. Electronic Journal of Structural Engineering, 14(1), 97-105. https://doi.org/10.56748/ejse.141931
Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve? In (Vol. 34, pp. 357-359): BMJ Publishing Group Ltd and the British Association for Accident. https://doi.org/10.1136/emermed-2017-206735
Ibrahim, A. A. M., & Tapamo, J.-R. (2024). A survey of vision-based methods for surface defects’ detection and classification in steel products. Paper presented at the Informatics. https://doi.org/10.3390/informatics11020025
Khan, S. U. R., Raza, A., Shahzad, I., & Ali, G. (2024). Enhancing concrete and pavement crack prediction through hierarchical feature integration with VGG-16 and triple classifier ensemble. Paper presented at the 2024 Horizons of Information Technology and Engineering (HITE). https://doi.org/10.1109/HITE63532.2024.10777242
Kim, B., Yuvaraj, N., Sri Preethaa, K., & Arun Pandian, R. (2021). Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Neural Computing and Applications, 33(15), 9289-9305. https://doi.org/10.1007/s00521-021-05690-8
Kim, J.-u., Oh, S.-J., & Shin, S.-c. (2024). A Study on the Welding Defect Detection Technique in Liquid Penetrant Test Image. Journal of the Korean Society of Industry Convergence, 27(6_3), 1751-1761. http://doi.org/10.21289/KSIC.2024.27.6.1751
Krishnan, S. S. R., Karuppan, M. N., Khadidos, A. O., Khadidos, A. O., Selvarajan, S., Tandon, S., & Balusamy, B. (2025). Comparative analysis of deep learning models for crack detection in buildings. Scientific Reports, 15(1), 2125. https://doi.org/10.1038/s41598-025-85983-3
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Lei, Q., Zhong, J., Wang, C., & Li, X. (2024). Integrating crack causal augmentation framework and dynamic binary threshold for imbalanced crack instance segmentation. Expert Systems with Applications, 240, 122552. https://doi.org/10.1016/j.eswa.2023.122552
Leung, M. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121-i129. https://doi.org/10.1093/bioinformatics/btu277
Lu, H., Li, Y., Zhang, Y., Chen, M., Serikawa, S., & Kim, H. (2017). Underwater optical image processing: a comprehensive review. Mobile networks and applications, 22(6), 1204-1211. https://doi.org/10.1007/s11036-017-0863-4
Ma, T., Sun, Z., & Chen, Q. (2018). Study on crack features in images of fluorescent magnetic particle inspection for railway wheelsets. Insight-non-destructive testing and condition monitoring, 60(9), 519-524. https://doi.org/10.1784/insi.2018.60.9.519
Minarno, A. E., Aripa, L., Azhar, Y., & Munarko, Y. (2023). Classification of malaria cell image using inception-v3 architecture. JOIV: International Journal on Informatics Visualization, 7(2), 273-278. https://doi.org/10.30630/joiv.7.2.1301
Padmapoorani, P., & Senthilkumar, S. (2023). Application of machine learning for crack detection on concrete structures using CNN architecture. Matéria (Rio de Janeiro), 28(1), e20230010. https://doi.org/10.1590/1517-7076-RMAT-2023-0010
Philip, R. E., Andrushia, A. D., Nammalvar, A., Gurupatham, B. G. A., & Roy, K. (2023). A comparative study on crack detection in concrete walls using transfer learning techniques. Journal of Composites Science, 7(4), 169. https://doi.org/10.3390/jcs7040169
Rainio, O., Teuho, J., & Klén, R. (2024). Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1), 6086. https://doi.org/10.1038/s41598-024-56706-x
Saul, L. K., & Roweis, S. T. (2003). Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of machine learning research, 4(Jun), 119-155. http://www.jmlr.org/papers/v4/saul03a.html
Seol, D., Kim, H., Park, S., & Lee, J. (2025). Comparative study of cnn-based deep learning models for concrete crack detection. Journal of Building Engineering, 113651. https://doi.org/10.1016/j.jobe.2025.113651
Serrati, D. S., Machado, M. A., Oliveira, J., & Santos, T. G. (2023). Non-destructive testing inspection for metal components produced using wire and arc additive manufacturing. Metals, 13(4), 648. https://doi.org/10.3390/met13040648
Shipway, N., Huthwaite, P., Lowe, M., & Barden, T. (2021). Using ResNets to perform automated defect detection for Fluorescent Penetrant Inspection. NDT & E International, 119, 102400. https://doi.org/10.1016/j.ndteint.2021.102400
Słoński, M., Schabowicz, K., & Krawczyk, E. (2020). Detection of flaws in concrete using ultrasonic tomography and convolutional neural networks. Materials, 13(7), 1557. https://doi.org/10.3390/ma13071557
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. https://doi.org/10.1109/CVPR.2016.308
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31(3), 759-776. https://doi.org/10.1007/s10845-019-01476-x
Tout, K., Meguenani, A., Urban, J.-P., & Cudel, C. (2021). Automated vision system for magnetic particle inspection of crankshafts using convolutional neural networks. The International Journal of Advanced Manufacturing Technology, 112(11), 3307-3326. https://doi.org/10.1007/s00170-020-06034-4
Wang, B., Zhong, S., Lee, T.-L., Fancey, K. S., & Mi, J. (2020). Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review. Advances in mechanical engineering, 12(4), 1687814020913761. https://doi.org/10.1177/1687814020913761
Wen, L., Li, X., Li, X., & Gao, L. (2019). A new transfer learning based on VGG-19 network for fault diagnosis. Paper presented at the 2019 IEEE 23rd international conference on computer supported cooperative work in design (CSCWD). https://doi.org/10.1109/CSCWD.2019.8791899
Wilkinson, S., & Duke, S. M. (2014). Comparative testing of radiographic testing, ultrasonic testing and phased array advanced ultrasonic testing non destructive testing techniques in accordance with the AWS D1. 5 bridge welding code. https://rosap.ntl.bts.gov/view/dot/27230
Wronkowicz, A., Dragan, K., & Lis, K. (2018). Assessment of uncertainty in damage evaluation by ultrasonic testing of composite structures. Composite Structures, 203, 71-84. https://doi.org/10.1016/j.compstruct.2018.06.104
Wu, Q., Qin, X., Dong, K., Shi, A., & Hu, Z. (2023). A learning-based crack defect detection and 3D localization framework for automated fluorescent magnetic particle inspection. Expert Systems with Applications, 214, 118966. https://doi.org/10.1016/j.eswa.2022.118966
Yang, Y., Yang, Y., Li, L., Chen, C., & Min, Z. (2022). Automatic defect identification method for magnetic particle inspection of bearing rings based on visual characteristics and high-level features. Applied Sciences, 12(3), 1293. https://doi.org/10.3390/app12031293
Zadeh, S. S., Khorshidi, M., & Kooban, F. (2024). Concrete surface crack detection with convolutional-based deep learning models. arXiv preprint arXiv:2401.07124. https://doi.org/10.48550/arXiv.2401.07124
Zou, J., Han, Y., & So, S.-S. (2008). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-59745-525-1_2

Details

Primary Language

English

Subjects

Image Processing, Planning and Decision Making

Journal Section

Research Article

Authors

Yasin Ulus
0009-0008-3859-8016
Türkiye

Mehmet Akif Şahman ^*
0000-0002-1718-3777
Türkiye

Publication Date

June 3, 2026

Submission Date

October 6, 2025

Acceptance Date

April 19, 2026

Published in Issue

Year 2026 Volume: 29 Number: 2

IZ

https://izlik.org/JA58DP96LL

Cite

RIS / Bibtex

APA

Ulus, Y., & Şahman, M. A. (2026). COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(2), 573-591. https://izlik.org/JA58DP96LL

AMA

1.Ulus Y, Şahman MA. COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION. KSU J. Eng. Sci. 2026;29(2):573-591. https://izlik.org/JA58DP96LL

Chicago

Ulus, Yasin, and Mehmet Akif Şahman. 2026. “COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 (2): 573-91. https://izlik.org/JA58DP96LL.

EndNote

Ulus Y, Şahman MA (June 1, 2026) COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 2 573–591.

IEEE

[1]Y. Ulus and M. A. Şahman, “COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION”, KSU J. Eng. Sci., vol. 29, no. 2, pp. 573–591, June 2026, [Online]. Available: https://izlik.org/JA58DP96LL

ISNAD

Ulus, Yasin - Şahman, Mehmet Akif. “COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29/2 (June 1, 2026): 573-591. https://izlik.org/JA58DP96LL.

JAMA

1.Ulus Y, Şahman MA. COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION. KSU J. Eng. Sci. 2026;29:573–591.

MLA

Ulus, Yasin, and Mehmet Akif Şahman. “COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 2, June 2026, pp. 573-91, https://izlik.org/JA58DP96LL.

Vancouver

1.Yasin Ulus, Mehmet Akif Şahman. COMPARATIVE STUDY OF IMAGE PROCESSING AND CNNS FOR SURFACE DEFECT DETECTION IN NUT PRODUCTION. KSU J. Eng. Sci. [Internet]. 2026 Jun. 1;29(2):573-91. Available from: https://izlik.org/JA58DP96LL