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基于机器视觉的台架上钢坯位置分割

Position Segmentation of Billet on Bench Based on Machine Vision
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摘要 钢坯通过航车从库存调度到台架,然后从台架经轨道到达炉前,以往是人工控制机械将台架上的钢坯推到轨道上的.这个过程的自动化实现需要知道钢坯在台架上的实时的位置分布,以便于自动控制推钢机.本文通过机器视觉方法实现台架上钢坯的实时定位,提出了以U-Net为基础网络,结合经典ResNet网络中的残差块,实现了钢坯横向位置的精确分割.实验结果和现场应用测试表明,本文方法的分割精度能够达到工业现场的控制需求. A billet is dispatched from the inventory to the bench by a crane and then from the bench to the front of the furnace through a track.In the past,the billet was pushed onto the track by the manual control of machinery.The automation of this process requires knowledge of the real-time position distribution of billets on the bench for automatic control of the pusher.In this study,the real-time positioning of billets on the bench is achieved by the machine vision method.Specifically,with the U-Net as the basic network,the residual blocks in classic ResNet are used to achieve the accurate segmentation of transverse positions of billets.The experimental results and field application tests indicate that the segmentation accuracy of this method can meet the control requirements of industrial fields.
作者 张哲 邵允学 吕刚 ZHANG Zhe;SHAO Yun-Xue;LYU Gang(School of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China;Shanghai Celi Engineering Technology Co.Ltd.,Shanghai 201900,China)
出处 《计算机系统应用》 2022年第10期254-260,共7页 Computer Systems & Applications
关键词 深度学习 残差网络 Res-UNet 目标分割 钢坯定位 deep learning residual network Res-UNet object segmentation billet positioning
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