基于卷积神经网络的锌渣识别方法研究  

Research on the Recognition Method of Zinc Slag based on Convolutional Neural Network

作　　者：邱意 陈劲杰[1] QIU Yi;CHEN Jinjie(School of Mechanic Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学机械工程学院,上海200093

出　　处：《软件工程》2021年第1期2-5,共4页Software Engineering

摘　　要：由于实际工业中工作池内钢水与表面锌渣存在部分重叠和边界不清以致识别较难的情况,提出了一种基于U-Net(U型神经网络)网络的锌渣识别方法。该方法先是把工业摄像头采集到的工作池图像进行灰度化,均值滤波等多种平滑模糊处理后,再采用完善的U-Net网络进行轮廓提取。接着将所得图像做二值化处理后,通过OpenCV(跨平台计算机视觉库)自带函数获得结果并对其进行分析。实验结果表明,基于U-Net的锌渣识别方法不仅能准确快速地区分钢水与表面锌渣,也能降低人工经验中存在的误差。In industry,the molten steel in working pool and surface slag are partially overlapped and the boundary is unclear,which makes it difficult to visually differentiate them.Aiming at this problem,the paper proposes a zinc slag recognition and segmentation method based on U-Net(U-shaped Neural Network).Firstly,the working pool images collected by industrial camera are smoothed and blurred by grayscale processing and average filtering.Then,perfect U-Net is used for contour extraction.Next,the obtained images are binarized.Finally,the result is obtained and analyzed by the built-in function of OpenCV(Computer Vision).The experimental results show that this zinc slag recognition method based on U-Net can accurately and quickly distinguish molten steel from surface zinc slag,as well as reduce errors in manual experience.

关 键 词：U-Net网络 锌渣识别 图像分割 深度学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]