Research on scrap classification and rating method based on SE attention mechanism

XIAO Peng-cheng; XU Wen-guang; ZHANG Yan; ZHU Li-guang; ZHU Rong; XU Yun-feng

doi:10.13374/j.issn2095-9389.2022.06.10.002

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Chinese Journal of Engineering > 2023 > 45(8): 1342-1352

XIAO Peng-cheng, XU Wen-guang, ZHANG Yan, ZHU Li-guang, ZHU Rong, XU Yun-feng. Research on scrap classification and rating method based on SE attention mechanism[J]. Chinese Journal of Engineering, 2023, 45(8): 1342-1352. doi: 10.13374/j.issn2095-9389.2022.06.10.002

Citation:

XIAO Peng-cheng, XU Wen-guang, ZHANG Yan, ZHU Li-guang, ZHU Rong, XU Yun-feng. Research on scrap classification and rating method based on SE attention mechanism[J]. Chinese Journal of Engineering, 2023, 45(8): 1342-1352. doi: 10.13374/j.issn2095-9389.2022.06.10.002

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Research on scrap classification and rating method based on SE attention mechanism

doi: 10.13374/j.issn2095-9389.2022.06.10.002

XIAO Peng-cheng^{1, 2},
XU Wen-guang¹,
ZHANG Yan^{1, 3},
ZHU Li-guang^{2, 4
,
,},
ZHU Rong²,
XU Yun-feng³

1.
College of Metallurgy and Energy, North China University of Science and Technology University, Tangshan 063210, China
2.
College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China
3.
Metallurgical and Ecological Engineering School, University of Science and Technology Beijing, Beijing 100083, China
4.
College of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

More Information

Corresponding author: E-mail: zhuliguang@ncst.edu.cn
Received Date: 2022-06-10
Available Online: 2022-09-19
Publish Date: 2023-08-25

Abstract

Abstract

Not only is scrap steel an indispensable ferritic raw material for the modern steel industry, but it is also the only green raw material that can replace iron ore in large quantities. The quality of the scrap steel directly affects the quality of molten steel, which makes it necessary to sort and grade scrap steel before it enters the furnace. Most iron and steel enterprises determine the grade of scrap steel mainly by visual inspection and caliper-based measurements by quality management personnel. As a result, this process is prone to human errors and low efficiency. Therefore, given that the major challenges of scrap inspection include the many categories of scrap, complex actual detection scenarios, and challenges in manual system connection, a deep learning network model CSSNet was proposed for scrap classification and rating based on the Squeeze-Excitation (SE) attention mechanism, and images of the scrap unloading process were collected for model training and validation. First, a 1∶3 physical model of scrap steel quality inspection was built to simulate this process. High-resolution visual sensors were used to collect images of diverse types of scrap steel in the scene of trucks unloading scrap steel. Then, a cross-stage local network was used to extract the features of the collected scrap images, the spatial pyramid structure was used to solve the problem of feature loss, and the attention mechanism was used to focus on the correlation between channels and retain the channel with the most feature information. Finally, model training and validation were done using two datasets containing seven labels for classification. In the model prediction stage, the constructed scrap steel quality inspection model CSSNet was used to judge the scrap steel category and quality to verify the accuracy and detection efficiency of the model. Based on the self-made scrap steel validation dataset, its performance was compared with mainstream single-stage object detection packages such as YOLOv4, YOLOv5s, and the two-stage object detection model Faster R-CNN. The model was found to be able to effectively rate different grades of scrap steel, with the classification accuracy rate of all categories has reached 83.7% and an mAP value of 88.8%. The performance of the CSSNet model is better than the other three target detection models. CSSNet can not only fully meet the needs of the actual production applications in terms of accuracy, real-time performance, and identification and rating efficiency but also surpass the traditional manual scrap quality inspection method, address multiple issues in the evaluation of scrap steel quality, and realize automated scrap steel quality testing.
- recycled iron and steel raw materials,
- scrap intelligent rating,
- deep learning,
- attention mechanism,
- cross stage partial networks

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References(27)

References

[1]	吳耀光, 肖步慶, 朱立光, 等. 電爐煉鋼鋼鐵原料的現狀分析與展望. 鋼鐵, 2021, 56(11):55 doi: 10.13228/j.boyuan.issn0449-749x.20210170 Wu Y G, Xiao B Q, Zhu L G, et al. Current situation analysis and prospect of iron and steel raw material for electric arc furnace steelmaking. Iron Steel, 2021, 56(11): 55 doi: 10.13228/j.boyuan.issn0449-749x.20210170
[2]	朱榮, 吳學濤, 魏光升, 等. 電弧爐煉鋼綠色及智能化技術進展. 鋼鐵, 2019, 54(8):9 doi: 10.13228/j.boyuan.issn0449-749x.20190188 Zhu R, Wu X T, Wei G S, et al. Development of green and intelligent technologies in electric arc furnace steelmaking processes. Iron Steel, 2019, 54(8): 9 doi: 10.13228/j.boyuan.issn0449-749x.20190188
[3]	上官方欽, 殷瑞鈺, 李煜, 等. 論中國發展全廢鋼電爐流程的戰略意義. 鋼鐵, 2021, 56(8):86 doi: 10.13228/j.boyuan.issn0449-749x.20200401 Shangguan F Q, Yin R Y, Li Y, et al. Dissussion on strategic significance of developing full scrap EAF process in China. Iron Steel, 2021, 56(8): 86 doi: 10.13228/j.boyuan.issn0449-749x.20200401
[4]	王仲亮, 顧超, 王敏, 等. 深度學習在煉鋼過程中的研究進展及應用現狀. 工程科學學報, http://kns.cnki.net/kcms/detail/10.1297.TF.20220506.1903.003.html Wang Z L, Gu C, Wang M, et al. Research progress and application status of deep learning in steelmaking process. Chin J Eng, http://kns.cnki.net/kcms/detail/10.1297.TF.20220506.1903.003.html
[5]	上官方欽, 酈秀萍, 周繼程, 等. 中國廢鋼資源發展戰略研究. 鋼鐵, 2020, 55(6):8 doi: 10.13228/j.boyuan.issn0449-749x.20190427 Shangguan F Q, Li X P, Zhou J C, et al. Strategic research on development of steel scrap resources in China. Iron Steel, 2020, 55(6): 8 doi: 10.13228/j.boyuan.issn0449-749x.20190427
[6]	Ma Y Q. Do iron ore, scrap steel, carbon emission allowance, and seaborne transportation prices drive steel price fluctuations? Resour Policy, 2021, 72: 102115 doi: 10.1016/j.resourpol.2021.102115
[7]	Smirnov N V, Rybin E I. Machine learning methods for solving scrap metal classification task // 2020 International Russian Automation Conference (RusAutoCon). Sochi, 2020: 1020
[8]	Gao Z J, Sridhar S, Spiller D E, et al. Applying improved optical recognition with machine learning on sorting Cu impurities in steel scrap. J Sustain Metall, 2020, 6(4): 785 doi: 10.1007/s40831-020-00300-8
[9]	Koyanaka S, Kobayashi K. Automatic sorting of lightweight metal scrap by sensing apparent density and three-dimensional shape. Resour Conserv Recycl, 2010, 54(9): 571 doi: 10.1016/j.resconrec.2009.10.014
[10]	Díaz-Romero D, Sterkens W, Van den Eynde S, et al. Deep learning computer vision for the separation of Cast-and Wrought-Aluminum scrap. Resour Conserv Recycl, 2021, 172: 105685 doi: 10.1016/j.resconrec.2021.105685
[11]	Li Y F, Qin X P, Zhang Z Y, et al. A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization. Waste Manag Res, 2021, 39(4): 573 doi: 10.1177/0734242X20987884
[12]	Chen S, Hu Z L, Wang C, et al. Research on the process of small sample non-ferrous metal recognition and separation based on deep learning. Waste Manag, 2021, 126: 266 doi: 10.1016/j.wasman.2021.03.019
[13]	Ramsurrun N, Suddul G, Armoogum S, et al. Recyclable waste classification using computer vision and deep learning // 2021 Zooming Innovation in Consumer Technologies Conference (ZINC). Novi Sad, 2021: 11
[14]	梅亞光. 基于機器視覺與LIBS技術的廢鋼智能分類研究[學位論文]. 北京: 北京科技大學, 2021 Mei Y G. Research on Intelligent Classification of Scrap Based on Machine Vision and LIBS Technology [Dissertation]. Beijing: University of Science and Technology Beijing, 2021
[15]	徐鋼, 黎敏, 徐金梧. 機器學習在深沖鋼質量自動判級中的應用. 工程科學學報, 2022, 44(6):1062 doi: 10.3321/j.issn.1001-053X.2022.6.bjkjdxxb202206011 Xu G, Li M, Xu J W. Application of machine learning in automatic discrimination of product quality of deep drawn steel. Chin J Eng, 2022, 44(6): 1062 doi: 10.3321/j.issn.1001-053X.2022.6.bjkjdxxb202206011
[16]	李江昀, 楊志方, 鄭俊鋒, 等. 深度學習技術在鋼鐵工業中的應用. 鋼鐵, 2021, 56(9):43 doi: 10.13228/j.boyuan.issn0449-749x.20210296 Li J Y, Yang Z F, Zheng J F, et al. Applications of iron and steel industry with deep learning technologies. Iron Steel, 2021, 56(9): 43 doi: 10.13228/j.boyuan.issn0449-749x.20210296
[17]	Wang D L, Zhang Y Q, Pan Y, et al. An automated inspection method for the steel box girder bottom of long-span bridges based on deep learning. IEEE Access, 2020, 8: 94010 doi: 10.1109/ACCESS.2020.2994275
[18]	Zhang L, Yang F, Zhang Y D, et al. Road crack detection using deep convolutional neural network // 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, 2016: 3708
[19]	Liu Z D, Li W X, Wei Z H. Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network. Text Res J, 2020, 90(9-10): 1057 doi: 10.1177/0040517519886032
[20]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need // Advances in Neural Information Processing Systems. Long Beach, 2017: 30
[21]	Wang C Y, Liao H Y M, Wu Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, 2020: 1571
[22]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84 doi: 10.1145/3065386
[23]	He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell, 2015, 37(9): 1904 doi: 10.1109/TPAMI.2015.2389824
[24]	Hu J, Shen L, Sun G. Squeeze-and-excitation networks // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, 2018: 7132
[25]	Zhu X K, Lyu S C, Wang X, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios // Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal, 2021: 2778
[26]	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, 2016: 770
[27]	Alzubaidi L, Zhang J L, Humaidi A J, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data, 2021, 8(1): 53 doi: 10.1186/s40537-021-00444-8