摘要
准确检测机械装配体在装配过程中的变化零部件,对于监测产品的装配顺序、提高装配质量、保障生产安全具有重要的意义。为了能够从多个角度检测机械装配体的变化零部件,提出基于三维注意力和双边滤波的机械装配体图像多视角变化检测网络(TAF Net)。为了提高机械装配体变化检测的准确性,TAF Net网络引入三维注意力机制,增强网络的细节特征提取能力;引入双边滤波,减少变化图像中的噪声,优化变化图像中零部件的边界。建立2个装配体变化检测数据集,分别为合成深度图像数据集、真实彩色图像数据集,使用2个数据集分别进行实验。结果表明:TAF Net网络能够精确检测出图像中的变化区域,在2个数据集中的综合评价指标F1_score都达到96%以上。
Accurately detecting the changed parts of the mechanical assembly during the assembly process is of great significance for monitoring the assembly sequence of the product,improving the assembly quality and ensuring production safety.In order to detect the changing parts of mechanical assembly from multiple angles,a multi-view change detection method(TAF Net) of mechanical assembly images based on three-dimensional attention and bilateral filtering network was proposed.In order to improve the accuracy of TAF Net detection of mechanical assembly changes,a 3D attention mechanism was introduced to enhance the network's detailed feature extraction capability;bilateral filtering was introduced to reduce noise in the images and optimize the boundaries of parts in the changed images.Two assembly change detection data sets were established,which were synthetic depth image data set and real color image data set.The two data sets were used for experiments.The experimental results show that the TAF Net network can accurately detect the change area in the images,the comprehensive evaluation index F1_score in the two data sets all reach more than 96%.
作者
岳耀帅
陈成军
李东年
官源林
洪军
赵正旭
YUE Yaoshuai;CHEN Chengjun;LI Dongnian;GUAN Yuanlin;HONG Jun;ZHAO Zhengxu(School of Mechanical and Automotive Engineering,Qingdao University of Technology,Qingdao Shandong 266520,China;School of Mechanical Engineering,Xi an Jiaotong University,Xi an Shaanxi 710049,China)
出处
《机床与液压》
北大核心
2023年第19期39-45,共7页
Machine Tool & Hydraulics
基金
国家自然科学基金面上项目(52175471)。
关键词
装配监测
变化检测
三维注意力
双边滤波
迁移学习
Assembly monitoring
Change detection
3D attention
Bilateral filtering
Transfer learning
