改进HED网络在轴承自动测量中的应用研究  

Research on application of improved HED network in automatic bearing measurement

作　　者：陈世奇 侯明[1] 李鹏程 Chen Shiqi;Hou Ming;Li Pengcheng(School of Automation,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学自动化学院,北京100192

出　　处：《电子测量技术》2024年第2期142-149,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(61971048)项目资助。

摘　　要：针对在工业环境中传统接触式测量轴承尺寸操作复杂且检测成本高的问题,提出一种新的加入注意力机制和结合Canny算法的改进型HED网络边缘检测算法。该方法基于HED网络,将主干网络第4、5个阶段的卷积层替换成连续空洞卷积,并设置网络的第3、4层池化步长为1,增大模型的感受野,提高输出的边缘图像精度;加入高效通道注意力机制ECA模块,有效抑制无关纹理特征和非边缘像素等影响;使用Canny算法中的非极大值抑制和双阈值处理算法,对检测的粗边缘进行细化,得到更加精确的轴承边缘;使用最小二乘圆拟合算法,获取轴承的内外环尺寸参数。实验结果表明,改进后的HED网络在ODS和OIS指标上分别达到了0.811和0.835,该方法可以有效实现轴承边缘检测并保证轴承尺寸测量精度。Aiming at the problems of complex operation and high cost of traditional contact measurement of bearing size in industrial environment,an improved edge detection algorithm for HED network is proposed by adding attention mechanism and Canny algorithm.The method is based on the HED network,the convolutional layer of the fourth and fifth stages of the backbone network is replaced with a continuous hollow convolutional layer,and the pooling step of the third and fourth layers of the network is set to 1,which increases the model receptive field and improves the output edge image accuracy.The efficient channel attention mechanism ECA module is added to effectively suppress the influence of irrelevant texture features and non-edge pixels.Using the non-maximum value suppression and double threshold processing algorithms in Canny algorithm,the coarse edges detected are refined to obtain more accurate bearing edges.The inner and outer diameter parameters of bearing are obtained by using the least square circle fitting method.The experimental results show that the improved HED model achieves 0.811 and 0.833 respectively in ODS and OIS,which can effectively realize the bearing edge detection and ensure the bearing size measurement accuracy.

关 键 词：HED网络 轴承边缘检测 尺寸测量 空洞卷积 注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]