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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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Acknowledgements

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

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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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