An efficient treatment method of scrap intelligent rating based on machine vision
Authors: Wenguang Xu, Pengcheng Xiao, Liguang Zhu, Guangsheng Wei, Rong Zhu
Conclusion
This paper introduces an efficient method for intelligent grading of waste steel based on machine vision. The method comprises the Deeplabv3 + carriage segmentation model, which reduces the impact of complex backgrounds in waste steel images on classification ratings, the CSBFNet model for precise waste steel classification ratings, and the SHAI image slicing model for efficient detection of small-sized waste steel at high resolutions. Experimental results dem- onstrate that our method achieves a mAP of 90.7% on the waste steel dataset and also performs excellently in gener- alization testing with new on-site data, providing accurate classification ratings for various categories. Comprehen- sive experiments validate the effectiveness of this method. The performance of the method meets practical produc- tion requirements and has undergone initial industrial application
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.