[5] [5] Luo Q , Sun Y , Li P , et al. Generalized completed local bi- nary patterns for time-efficient steel surface defect classifi- cation [ J ] . IEEE Transactions on Instrumentation and Measurement , 2018 , 68(3) : 667-679.

[6] [6] Ren S Q , He K M , Sun J. Faster R-CNN : Towards Real- Time Object Detection with Region Proposal Networks[J] . IEEE transactions on pattern analysis and machine intelli- gence , 2017 , 39(6) ,1137- 1149.

[7] [7] Zhao W D , Chen F , Huang H C , et al. A New Steel Defect Detection Algorithm Based on Deep Learning[J] . Compu- tational Intelligence and Neuroscience , 2021 , 2021(2021) :1687-5265.

[10] [10] Redmon J , Divvala S K , Girshick , et al. You Only Look Once : Unified , Real-Time Object Detection [ C]// IEEE Conference on Neural Information ,2015.

[11] [11] Liu W , Anguelov D , Erhan D , Szegedy C , et al. SSD : Single Shot MultiBox Detector[J] . International Journal of Engineering Intelligent Systems for Electrical and Communi- cations. 2016 ,14(3) :21-37.

[12] [12] He K M , Zhang X Y , Ren S Q , et al. Spatial pyramid poo- ling in deep convolutional networks for visual recognition[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015 ,37(9) :1904- 1916.

[15] [15] Li S , Wang X Q. YOLOv5-based Defect Detection Model for Hot Rolled Strip Steel[J] . Journal of Physics : Confer- ence Series ,2022 , 2171(1) ,1742-6588.

[16] [16] Wang C Y , Bochkovskiy A , Liao H. YOLOv7 : trainable bag-of-freebies sets new state-of-the-art for real-time ob- ject detectors [ EB/OL] . (2022-07-06) [2023-2-23] .

[17] [17] Liu Z , Mao H Z , Wu C Y , et al. A ConvNet for the 2020s[J] . arXiv : 2201. 03545.

[18] [18] Tan M , Le Q V , MixConv : Mixed depthwise convolutional kernels[J] . arXiv preprint arXiv : 1907. 09595 ,2019.

[19] [19] Yang L X , Zhang R Y , Li L D , et al. SimAM : A Simple , Parameter-Free Aattention Module for Convolutional Neural Networks[ C]//Proceedings of the 38th International Con- ference on Machine Learning , 2021 :11863- 11874.