An Improved Faster Region-based Convolutional Neural Network Algorithm for Identification of Steel Coil End-head

被引：1
|
作者：
Pan, Jian-Zhou [1 ,2 ,3 ]
Yang, Chi-Hsin [2 ]
Wu, Long [2 ]
Tang, Wen-Hu [4 ]
Wang, Kung-Chieh [2 ]
机构：
[1] Univ Sci & Technol, Sch Mat Sci & Engn, Beijing 100083, Peoples R China
[2] Sanming Univ, Sch Mech & Elect Engn, Sanming 365004, Fujian, Peoples R China
[3] Fujian Sansteel Grp Co Ltd, Sanming 365004, Fujian, Peoples R China
[4] Fuzhou Univ, Sch Mech Engn & Automat, Fuzhou 350108, Fujian, Peoples R China
来源：
SENSORS AND MATERIALS | 2023年 / 35卷 / 10期
关键词：
steel coil end-head; improved faster region -based convolutional neural network (F-RCNN); algorithm; deep learning; feature pyramid network (FPN); parallel attention module (PAM);
D O I：
10.18494/SAM4589
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
A method that uses machine vision and machine learning technologies to identify the endhead in a steel coil has seldom been proposed. In this study, an improved faster region-based convolutional neural network (F-RCNN) deep learning algorithm is introduced to identify the position of the steel coil end-head for a hardware system set up for image sensing and detection. The feature pyramid network (FPN) and the parallel attention module (PAM), which are both involved in the traditional F-RCNN, are used to increase the detection accuracy. Our experimental results validated the effectiveness of the proposed improved algorithm.
引用
收藏
页码：4653 / 4669
页数：17
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