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Automatic recognition and intelligent analysis of central shrinkage defects of continuous casting billets based on deep learning 被引量:2

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摘要 The internal quality inspection of the continuous casting billets is very important,and mis-inspection will seriously affect the subsequent production process.The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets.The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9.We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length,width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets.The results show that all the testing images are rated correctly,and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.
出处 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第5期937-948,共12页
基金 the National Natural Science Foundation of China(Nos.U21A20117,U1560207 and 52003039) the National Key R&D Program of China(No.2017YFB0304402) the Fundamental Research Funds for the Central Universities(Nos.N2125018 and N2109001) the Liaoning Natural Science Foundation(No.2022-MS-365) the Liaoning Innovative Research Team in University(No.LT2017011).
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