International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 18, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1689KB)
Optimized Classification of Steel Surface Defects via Hybrid Features and Neighborhood Component Analysis
PDF (1689KB), PP.71-91
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Author(s)
Ritu Juneja
1,*
Anil Dudy
1
Index Terms
DTCWT, Ensemble AdaBoost, ESO, MSLBP, NCA, SVM
Abstract
This paper establishes a new process of surface defect detection of steel products with both integrated image processing and image vision capabilities. The approach which incorporates Multi-Scale Local Binary Pattern (MSLBP), Dual-Tree Complex Wavelet Transform (DTCWT), and Gabor Wavelet in extracting features, whilst the Neighborhood Component Analysis (NCA) approach is in selecting the features. Ensemble AdaBoost is employed as a comparative baseline classifier and the final defect detection performance is presented in the Enhanced Snake Optimized Support Vector Machines (ESO-SVM) model. The suggested approach is superior to the classical methods, as the results of the experiments show 98.8 percent accuracy and 98.5 percent F1-score at the process of detecting fine and irregular defects under different production conditions. The system improves reliability and scalability of automatic defect detection thus increasing the quality of steel products and decreasing wastes.
Cite This Paper
Ritu Juneja, Anil Dudy, "Optimized Classification of Steel Surface Defects via Hybrid Features and Neighborhood Component Analysis", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.18, No.3, pp. 71-91, 2026. DOI:10.5815/ijigsp.2026.03.04
Reference
[1]Sime, D.M., Wang, G., Zeng, Z. and Peng, B., 2024. Deep learning-based automated steel surface defect segmentation: a comparative experimental study. Multimedia Tools and Applications, 83(1), pp.2995-3018.
[2]Wang, G.Q., Zhang, C.Z., Chen, M.S., Lin, Y.C., Tan, X.H., Kang, Y.X., Wang, Q., Zeng, W.D. and Zhao, W.W., 2024. A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection. Advanced Engineering Informatics, 59, p.102280.
[3]Tang, B., Chen, L., Sun, W. and Lin, Z.K., 2023. Review of surface defect detection of steel products based on machine vision. IET Image Processing, 17(2), pp.303-322.
[4]Long, Y., Zhang, J., Huang, S., Peng, L., Wang, W., Wang, S. and Zhao, W., 2022. A novel crack quantification method for ultra-high-definition magnetic flux leakage detection in pipeline inspection. IEEE Sensors Journal, 22(16), pp.16402-16413.
[5]Santoro, L., Sesana, R., Molica Nardo, R. and Curá, F., 2023. Infrared in-line monitoring of flaws in steel welded joints: a preliminary approach with SMAW and GMAW processes. The International Journal of Advanced Manufacturing Technology, 128(5), pp.2655-2670.
[6]Akhyar, F., Lin, C.Y., Muchtar, K., Wu, T.Y. and Ng, H.F., 2019, September. High efficient single-stage steel surface defect detection. In 2019 16th IEEE international conference on advanced video and signal based surveillance (AVSS) (pp. 1-4). IEEE.
[7]Qu, Y., Wan, B., Wang, C., Ju, H., Yu, J., Kong, Y. and Chen, X., 2023. Optimization algorithm for steel surface defect detection based on PP-YOLOE. Electronics, 12(19), p.4161.
[8]Akhyar, F., Liu, Y., Hsu, C.Y., Shih, T.K. and Lin, C.Y., 2023. FDD: a deep learning–based steel defect detectors. The International Journal of Advanced Manufacturing Technology, 126(3), pp.1093-1107.
[9]Shao, Y., Fan, S., Sun, H., Tan, Z., Cai, Y., Zhang, C. and Zhang, L., 2023. Multi-scale lightweight neural network for steel surface defect detection. Coatings, 13(7), p.1202.
[10]Hussain, M., 2023. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines, 11(7), p.677.
[11]Üzen, H., Turkoglu, M., Aslan, M. and Hanbay, D., 2023. Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection. The Visual Computer, 39(5), pp.1745-1764.
[12]Yang, L., Xu, S., Fan, J., Li, E. and Liu, Y., 2023. A pixel-level deep segmentation network for automatic defect detection. Expert Systems with Applications, 215, p.119388.
[13]Zhou, G. and Sun, H., 2020, June. Defect detection method for steel based on semantic segmentation. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 975-979). IEEE.
[14]Aydin, I., Güçlü, E. and Akin, E., 2023, November. An Improved Unsupervised Convolutional Neural Networks for Detection of Steel Wire Defects. In 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 402-407). IEEE.
[15]Xu, R., Hao, R. and Huang, B., 2022. Efficient surface defect detection using self-supervised learning strategy and segmentation network. Advanced Engineering Informatics, 52, p.101566.
[16]Vasan, V., Sridharan, N.V., Vaithiyanathan, S. and Aghaei, M., 2024. Detection and classification of surface defects on hot-rolled steel using vision transformers. Heliyon, 10(19).
[17]SD-Saliency 900 dataset. Online Available at: https://www.kaggle.com/datasets/alex000kim/sdsaliency900
[18]Kaggle, "Severstal: Steel Defect Detection," 2020. [Online]. Available: https://www.kaggle.com/competitions/severstal-steel-defect-detection [Accessed: 16-Jun-2025].