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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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作者 Gong-hao Lian Qi-hao Sun +6 位作者 Xiao-ming Liu Wei-miao Kong Ming Lv Jian-jun Qi Yong Liu ben-ming yuan Qiang Wang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第5期937-948,共12页
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 semant... 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. 展开更多
关键词 Central shrinkage Deep learning Image segmentation Circumscribed rectangle Automatic recognition
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