[1]

K. Song and Y. Yan, "A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects," Applied Surface Science, pp. 858-864, 2013.

[2]

G. K. Nand and N. Neogi, "Defect detection of steel surface using entropy segmentation," in In 2014 Annual IEEE India Conference (INDICON) , 2014.

[3]

Y. Liu, K. Xu and D. Wang, "Online surface defect identification of cold rolled strips based on local binary pattern and extreme learning machine," Metals, p. 197, 2018.

[4]

S. Wang, X. Xia, L. Ye and B. Yang, "Automatic detection and classification of steel surface defect using deep convolutional neural networks," Metals, p. 388, 2021.

[5]

Z. Guo, C. Wang, G. Yang, Z. Huang and G. Li, "Msft-yolo: Improved yolov5 based on transformer for detecting defects of steel surface," Sensors, p. 3467, 2022.

[6]

R. Tian and M. Jia, "DCC-CenterNet: A rapid detection method for steel surface defects," Measurement, p. 110211, 2022.

[7]

L. Shang, Q. Yang, J. Wang, S. Li and W. Lei, "Detection of rail surface defects based on CNN image recognition and classification," in In 2018 20th International Conference on Advanced Communication Technology (ICACT) , 2018.

[8]

W. Chen, Y. Gao, L. Gao and X. Li, "A new ensemble approach based on deep convolutional neural networks for steel surface defect classification," Procedia CIRP, pp. 1069-1072, 2018.

[9]

M. W. Akram, G. Li, Y. Jin, X. Chen, C. Zhu, X. Zhao and A. Ahmad, "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, p. 116319, 2019.

[10]

Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu and X. Li, "Triplet-graph reasoning network for few-shot metal generic surface defect segmentation," IEEE Transactions on Instrumentation and Measurement, pp. 1-11, 2021.

[11]

R. Neven and T. Goedemé, "A multi-branch U-Net for steel surface defect type and severity segmentation," Metals, p. 870, 2021.

[12]

Y. Aslam, N. Santhi, N. Ramasamy and K. Ramar, "Localization and segmentation of metal cracks using deep learning," Journal of Ambient Intelligence and Humanized Computing, pp. 4205-4213, 2021.

[13]

X. Cheng and J. Yu, "RetinaNet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection.," IEEE Transactions on Instrumentation and Measurement, pp. 1-11, 2020.

[14]

X. Kou, S. Liu, K. Cheng and Y. Qian, "Development of a YOLO-V3-based model for detecting defects on steel strip surface," Measurement, p. 109454, 2021.

[15]

Q. Ren, J. Geng and J. Li, "Slighter Faster R-CNN for real-time detection of steel strip surface defects," in 2018 Chinese Automation Congress (CAC) , 2018.

[16]

A. Bochkovskiy, C. Y. Wang and H. Y. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," 2020. [Online]. Available: arXiv:2004.10934.

[17]

K. He, X. Zhang, S. Ren and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," in IEEE transactions on pattern analysis and machine intelligence, 2015.

[18]

S. Liu, L. Qi, H. Qin, J. Shi and J. Jia, "Path aggregation network for instance segmentation," in In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.

[19]

R. Sunkara and T. Luo, "No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects," in In Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2022.

[20]

M. Siliang and X. Yong, "MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression.," 2023. [Online]. Available: arXiv:2307.07662.

[21]

D. Tabernik, S. Šela, J. Skvarč and D. Skočaj, "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, pp. 759-776, 2020.

[22]

R. Vorobel, I. Ivasenko, O. Berehulyak and T. Mandzii, "Segmentation of rust defects on painted steel surfaces by intelligent image analysis.," Automation in Construction, p. 103515, 2021.