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An efficient treatment method of scrap intelligent rating based on machine vision

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Abstract

Scrap steel is a green resource that can substitute iron ore and is an important raw material in the modern steel industry. To address the many issues such as high risk, low accuracy in grading, and the susceptibility to questioning fairness in the manual inspection process of scrap steel, we propose an efficient intelligent scrap steel classification method based on machine vision, achieving accurate classification and grading of nine types of scrap steel. Firstly, a scrap steel quality inspection system was established at the scrap steel recycling site, where images of various types of scrap steel were collected and various image processing methods were employed for preprocessing, leading to the establishment of scrap steel datasets and carriage segmentation datasets. Secondly, a carriage segmentation model was built based on image segmentation technology to significantly reduce the influence of complex backgrounds of scrap steel images on classification and grading. Subsequently, an intelligent scrap steel classification grading model was established based on the attention mechanism in deep learning, combined with the Spatially Adaptive Heterogeneous Image Slicing (SAHI) image slicing prediction method, achieving accurate classification and grading of scrap steel under complex backgrounds and high-resolution images in scrap steel recycling. Finally, we conducted tests on the proposed method. Experimental results demonstrate the good generalization of our proposed method, accurately detecting various types of scrap steel, meeting the requirements of accuracy, real-time performance, and good generalization in scrap steel recycling classification and grading, achieving initial industrial application, and exhibiting significant advantages compared to traditional manual scrap steel quality inspection.

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References

  1. Tsai W-H, Lan S-H, Huang C-T (2019) Activity-based Standard Costing Product-Mix decision in the Future Digital era: Green Recycling Steel-Scrap Material for Steel Industry. Sustainability 11(3):899. https://doi.org/10.3390/su11030899

    Article  Google Scholar 

  2. Liu, Yang M, Cui, Gao X (2023) Building up scrap steel bases for perfecting scrap steel industry chain in China: an evolutionary game perspective. Energy 278. 127742. https://doi.org/10.1016/j.energy.2023.127742

  3. Dworak S, and Johann Fellner (2021) Steel scrap generation in the EU-28 since 1946–Sources and composition. Resources, conservation and recycling. 173:105692. https://doi.org/10.1016/j.resconrec.2021.105692

  4. Kang Z et al (2022) Carbon neutrality orientates the reform of the steel industry. Nature Materials 21:1094–1098. https://doi.org/10.1038/s41563-022-01370-7

  5. Panasiuk D et al (2022) International comparison of impurities mixing and accumulation in steel scrap. J Ind Ecol 26(3):1040–1050. https://doi.org/10.1111/jiec.13246

    Article  Google Scholar 

  6. Fan Z, Julio S, Friedmann Low-carbon production of iron and steel: technology options, economic assessment, and policy. Joule 5(4): 829–862. https://doi.org/10.1016/j.joule.2021.02.018

  7. Ma Y (2021) Do iron ore, scrap steel, carbon emission allowance, and seaborne transportation prices drive steel price fluctuations? Resour Policy 72:102115. https://doi.org/10.1016/j.resourpol.2021.102115

    Article  Google Scholar 

  8. Xi X et al (2020) Melting characteristics of steel scrap with different carbon contents in liquid steel. Ironmak Steelmaking 47(10):1087–1099. https://doi.org/10.1080/03019233.2020.1799671

    Article  Google Scholar 

  9. Dworak S, Rechberger H, Johann Fellner (2022) How will tramp elements affect future steel recycling in Europe?–A dynamic material flow model for steel in the EU-28 for the period 1910 to 2050. Resources, conservation and recycling. 179:106072. https://doi.org/10.1016/j.resconrec.2021.106072

  10. Leão A, Souza et al (2023) Rigorous environmental and energy life cycle assessment of blast furnace pig iron in Brazil: the role of carbon and iron sources, and co-product utilization. Sustainable Mater Technol 36:e00607. https://doi.org/10.1016/j.susmat.2023.e00607

    Article  Google Scholar 

  11. Suer J, Traverso M, Jäger N (2022). Review of Life Cycle Assessments for Steel and Environmental Analysis of Future Steel Production Scenarios. Sustainability 14(21):14131 https://doi.org/10.3390/su142114131

  12. Futas P et al (2022) Metallurgical quality of cast Iron made from Steel Scrap and possibilities of its improvement. Metals 13. 127. https://doi.org/10.3390/met13010027

  13. Yue Q et al (2023) Analysis of iron and steel production paths on the energy demand and carbon emission in China’s iron and steel industry. Environ Dev Sustain 25(5):4065–4085. https://doi.org/10.1007/s10668-022-02234-5

    Article  Google Scholar 

  14. Lin Y et al (2021) Low-carbon development for the iron and steel industry in China and the world: Status Quo. Future Vis Key Actions Sustain 13(22):12548. https://doi.org/10.3390/su132212548

    Article  Google Scholar 

  15. Nechifor V, Calzadilla A, Bleischwitz R, Winning M, Tian X, Usubiaga A (2020) Steel in a circular economy: global implications of a green shift in China. World Dev 127:104775. https://doi.org/10.1016/j.worlddev.2019.104775

    Article  Google Scholar 

  16. Yiqing JIA, Hongmei DUAN, Qunqi LIU (2021) China Min Magazine 30(3):31–36. https://doi.org/10.12075/j.issn.1004-4051.2021.03.024. Scrap steel resource utilization trend in China: Analysis and forecast from 2020 to 2035.

  17. Wang Y et al (2023) Decarbonization pathways of China’s iron and steel industry toward carbon neutrality. Resources. Conserv Recycling 194:106994. https://doi.org/10.1016/j.resconrec.2023.106994

    Article  Google Scholar 

  18. Liu Y et al (2022) Multi-objective coordinated development paths for China’s steel industry chain based on water-energy-economy dependence. J Clean Prod 370:133421. https://doi.org/10.1016/j.jclepro.2022.133421

    Article  Google Scholar 

  19. Cao Q, Beden S, Beckmann A (2022) A core reference ontology for steelmaking process knowledge modelling and information management. Comput Ind 135:103574. https://doi.org/10.1016/j.compind.2021.103436

    Article  Google Scholar 

  20. Grosso M, Motta A, Rigamonti L (2010) Efficiency of energy recovery from waste incineration, in the light of the new Waste Framework Directive. Waste Manag 30(7):1238–1243. https://doi.org/10.1016/j.wasman.2010.02.036

    Article  Google Scholar 

  21. Riley WD, Brown RE, Soboroff DM (1983) Rapid identification and sorting of scrap metals. Conserv Recycl 6:181–192. https://doi.org/10.1016/0361-3658(83)90004-8

    Article  Google Scholar 

  22. Mesina MB, de Jong TPR, Dalmijn WL (2004) New developments on sensors for quality control and automatic sorting of non-ferrous metals. IFAC Proceedings Volumes. 37:293–298. https://doi.org/10.1016/S1474-6670(17)31039-X

  23. Spencer DB (2005) The high-speed identification and sorting of nonferrous scrap. JOM 57:46–51. https://doi.org/10.1007/s11837-005-0081-6

    Article  Google Scholar 

  24. Cuce E, Cuce PM, Guclu T, Besir A, Gokce E, Serencam U (2018) A novel method based on thermal conductivity for material identification in scrap industry: an experimental validation. Measurement 127:379–389. https://doi.org/10.1016/j.measurement.2018.06.014

    Article  Google Scholar 

  25. Brooks L, Gaustad G (2021) The potential for XRF & LIBS handheld analyzers to perform material characterization in scrap yards. J Sustainable Metall 7:732–754. https://doi.org/10.1007/s40831-021-00361-3

    Article  Google Scholar 

  26. Tran-Quang V, Dao-Viet H (2022) An internet of radiation sensor system (IoRSS) to detect radioactive sources out of regulatory control. Sci Rep 12(1):7195. https://doi.org/10.1038/s41598-022-11264-y

    Article  Google Scholar 

  27. Auer M, Osswald K, Volz R, Woidasky J (2019) Artificial intelligence-based process for metal scrap sorting, arXiv preprint arXiv:1903.09415. https://doi.org/10.48550/arXiv.1903.09415

  28. Kashiwakura S (2020) Selection of atomic emission lines on the mutual identification of austenitic stainless steels with a combination of laser-induced breakdown spectroscopy (LIBS) and partial-least-square regression (PLSR). ISIJ Int 60(6):1245–1253. https://doi.org/10.2355/isijinternational.ISIJINT-2019-549

    Article  Google Scholar 

  29. Li Y, Qin X, Zhang Z, Dong H (2021) A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization. Waste Manag Res 39(4):573–583. https://doi.org/10.1177/0734242X20987884

    Article  Google Scholar 

  30. Diaz-Romero D, Jossue et al (2022) Classification of Aluminum Scrap by Laser Induced Breakdown Spectrometry (Libs) and Rgb + D Image Fusion Using Deep Learning Approaches. Available at SSRN 4272447 https://doi.org/10.2139/ssrn.4272447

  31. Park S, Lee J, Kwon E et al (2022) 3D sensing System for Laser-Induced Breakdown Spectroscopy-based metal scrap identification. Int J Precis Eng Manuf -Green Tech 9:695–707. https://doi.org/10.1007/s40684-021-00364-1

    Article  Google Scholar 

  32. Zeng H, Zhang Z, Liu S (2023) A hybrid approach for metal element identification by using laser-induced breakdown spectroscopy data. In Earth and Space: From Infrared to Terahertz (ESIT 2022), 137–143. SPIE. https://doi.org/10.1117/12.2664527

  33. Koyanaka S, Kobayashi K (2011) Incorporation of neural network analysis into a technique for automatically sorting lightweight metal scrap generated by ELV shredder facilities. Resour Conserv Recycling 55 5 515–523. https://doi.org/10.2355/isijinternational.ISIJINT-2019-549

  34. Pal SK et al (2021) Deep learning in multi-object detection and tracking: state of the art. Appl Intell 51:6400–6429. https://doi.org/10.1007/s10489-021-02293-7

    Article  Google Scholar 

  35. Heo Y-J, Yeo W-H, Byung-Gyu K (2023) Deepfake detection algorithm based on improved vision transformer. Appl Intell 53(7):7512–7527. https://doi.org/10.1007/s10489-022-03867-9

    Article  Google Scholar 

  36. Daigo I, Murakami K, Tajima K et al (2023) Thickness classifier on steel in heavy melting scrap by deep-learning-based image analysis. ISIJ Int 63:197–203. https://doi.org/10.2355/isijinternational.ISIJINT-2022-331

    Article  Google Scholar 

  37. Jhaldiyal A, Chaudhary N (2023). Semantic segmentation of 3D LiDAR data using deep learning: a review of projection-based methods. Appl Intell 53(6):6844–6855. https://doi.org/10.1007/s10489-022-03930-5

  38. Gao Z et al (2023) An RGB-D-Based thickness feature descriptor and its application on scrap steel grading. IEEE Trans Instrum Meas 72:1–14. https://doi.org/10.1109/TIM.2023.3328089

    Article  Google Scholar 

  39. Xu W, Xiao P, Zhu L, Zhang Y, Chang J, Zhu R, Xu Y (2023) Classification and rating of steel scrap using deep learning. Eng Appl Artif Intell 123:106241. https://doi.org/10.1016/j.engappai.2023.106241

    Article  Google Scholar 

  40. Tu Q et al (2022) Automated scrap steel grading via a hierarchical learning-based framework. IEEE Trans Instrum Meas 71:1–13. https://doi.org/10.1109/TIM.2022.3206816

    Article  Google Scholar 

  41. Yi C, Chen Q, Xu B, Huang T (2023) Steel Strip defect Sample Generation Method based on fusible feature GAN Model under few samples. Sensors 23(6):3216. https://doi.org/10.3390/s23063216

    Article  Google Scholar 

  42. Zhang Y et al (2022) A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes. https://doi.org/10.1016/j.heliyon.2022.e11623. Heliyon 8.11

  43. Wang J et al (2021) Investigating the effect of laser cutting parameters on the cut quality of Inconel 625 using response surface method (RSM). Infrared Phys Technol 118:103866. https://doi.org/10.1016/j.infrared.2021.103866

    Article  Google Scholar 

  44. Rawa, Muhyaddin JH et al (2023) Using the numerical simulation and artificial neural network (ANN) to evaluate temperature distribution in pulsed laser welding of different alloys. Eng Appl Artif Intell 126:107025. https://doi.org/10.1016/j.engappai.2023.107025

    Article  Google Scholar 

  45. Sun C et al (2023) Systematic evaluation of pulsed laser parameters effect on temperature distribution in dissimilar laser welding: a numerical simulation and artificial neural network. Opt Laser Technol 163:109407. https://doi.org/10.1016/j.optlastec.2023.109407

    Article  Google Scholar 

  46. Dehkordi MH, Razavi et al (2023) Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity. Heliyon 9.6 https://doi.org/10.1016/j.heliyon.2023.e17539

  47. Azimy H et al (2023) Analysis of thermal performance and ultrasonic wave power variation on heat transfer of heat exchanger in the presence of nanofluid using the artificial neural network: experimental study and model fitting. J Therm Anal Calorim 148(16):8009–8023. https://doi.org/10.1007/s10973-022-11827-1

    Article  Google Scholar 

  48. Xiao Pcheng, Wen-guang XU, Yan ZHANG, Li-guang ZHU, Rong ZHU, Yun-feng XU (2023) Research on scrap classification and rating method based on SE attention mechanism[J]. Chin J Eng 45(8):1342–1352. https://doi.org/10.13374/j.issn2095-9389.2022.06.10.002

    Article  Google Scholar 

  49. Tian Y et al (2023) A fault diagnosis method for few-shot industrial processes based on semantic segmentation and hybrid domain transfer learning. Appl Intell 53(23):28268–28290. https://doi.org/10.1007/s10489-023-04979-6

    Article  Google Scholar 

  50. Zhou J, and Jianbo Yu (2021) Chisel edge wear measurement of high-speed steel twist drills based on machine vision. Comput Ind 128:103436. https://doi.org/10.1016/j.compind.2021.103574

    Article  Google Scholar 

  51. Tian C et al (2020) Deep learning on image denoising: an overview. Neural networks 131. 251–275. https://doi.org/10.1016/j.neunet.2020.07.025

  52. Ding K et al (2020) Image quality assessment: unifying structure and texture similarity. IEEE transactions on pattern analysis and machine intelligence 44.5. 2567–2581. https://doi.org/10.1109/TPAMI.2020.3045810

  53. Huang Z et al (2021) Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization. Optik 226. 165877. https://doi.org/10.1016/j.ijleo.2020.165877

  54. Chen L-C, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV). https://doi.org/10.48550/arXiv.1802.02611

  55. Siddique N et al (2021) U-net and its variants for medical image segmentation: a review of theory and applications. Ieee Access 9:82031–82057. https://doi.org/10.1109/ACCESS.2021.3086020

    Article  Google Scholar 

  56. Akyon F, Cagatay SO, Altinuc, Temizel A (2022) Slicing aided hyper inference and fine-tuning for small object detection. 2022 IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/ICIP46576.2022.9897990

  57. Zhuxi MA et al (2022) A lightweight detector based on attention mechanism for aluminum strip surface defect detection. Comput Ind 136:103585. https://doi.org/10.1016/j.compind.2021.103585

    Article  Google Scholar 

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Acknowledgements

The research was completed under the National Natural Science Foundation of China (Key Program): U2A20114.

Author information

Authors and Affiliations

  1. College of Metallurgy and Energy, North China University of Science and Technology University, Tangshan, Hebei, 063210, China

    Wenguang Xu & Pengcheng Xiao

  2. School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, China

    Liguang Zhu

  3. HBIS Group Co., Ltd (HBIS), Hebei, 050000, China

    Pengcheng Xiao

  4. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Pengcheng Xiao, Liguang Zhu, Guangsheng Wei & Rong Zhu

  5. National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, 100083, China

    Wenguang Xu

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Contributions

Wenguang Xu: Ideas; Creation of models; Writing- Original draft preparation. Pengcheng Xiao: Formulation or evolution of overarching research goals and aims; Provision of study materials; Reviewing and Editing. Liguang Zhu: Supervision; Provision of computing resources. Guangsheng Wei: Management and coordination responsibility for the planning and execution of research activities. Rong Zhu: Provision of laboratory samples; Conducting a research and investigation process.

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Correspondence to Pengcheng Xiao.

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Xu, W., Xiao, P., Zhu, L. et al. An efficient treatment method of scrap intelligent rating based on machine vision. Appl Intell 54, 10912–10928 (2024). https://doi.org/10.1007/s10489-024-05581-0

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