期刊
JOURNAL OF INSTRUMENTATION
卷 16, 期 11, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1748-0221/16/11/P11041
关键词
Inspection with x-rays; X-ray detectors
资金
- Fundamental Research Funds for the Central Universities [NS2018042]
- National Natural Science Foundation of China [11975121, 11775113, 11705088]
- National Safety Academic Fund [U1930125]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX20_0195]
In this study, a method based on multi-energy X-ray transmission and machine learning was proposed for the accurate identification and classification of magnesium and aluminum in scrap metal recycling, achieving high recognition rates of 96.43% and 98.81% respectively.
The identification of magnesium and aluminum in scrap metal recycling has always been a difficult point. In this paper, a material identification method of multi-energy X-ray transmission (ME-XRT) based on photon counting detector (PCD) and machine learning algorithm was proposed and used to identify and classify magnesium and aluminum. This method includes three main steps: using PCD to obtain X-ray attenuation images of five energy bins, feature extraction, and the machine learning classification. The performance of several machine learning models was compared for the fine-grained classification task. The prediction results demonstrate that the best achieved recognition rates of aluminum and magnesium are 96.43% and 98.81%, respectively.
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