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作 者:陈冠琪 胡国清[1] Jahangir Alam SM 许华忠[1] 李开林[1] 戈明亮[1] 易玉华[1] 罗建中 CHEN Guanqi;HU Guoqing;Jahangir Alam SM;XU Huazhong;LI Kailin;GE Mingliang;YI Yuhua;LUO Jianzhong(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China)
机构地区:[1]华南理工大学机械与汽车工程学院
出 处:《新技术新工艺》2019年第8期72-76,共5页New Technology & New Process
摘 要:在零件的装配、分拣、焊接等工业生产过程中,使用图像处理的方法对零件进行识别能够大大减少夹具的使用。在零件的图像识别领域中,提出一种基于多尺度融合的MDSSD零件识别系统,MDSSD网络以SSD网络为基础,通过针对特定的零件识别场景,特别是小目标零件,使用融合模块实现深层网络和浅层网络的跨层连接,并且实现类别的预测以及坐标的回归。在特定环境的螺钉、螺母、垫圈的测试数据集上,分别使用SSD网络和MDSSD网络对单张图片的单类零件和单张图片的多类零件进行检测操作。实验表明,使用MDSSD算法平均检测精度达到97.11%,检测速度达到32.00 fps,MDSSD算法对比原来的SSD算法,平均检测精度提高了6.21%,能实时高精度地检测出零件,满足零件识别需求,可解决工业实际问题。In the assembly,sorting,welding and other industrial processes of parts,using image processing method to identify parts can greatly reduce the use of fixtures.In the image recognition field,a kind of parts recognition system based on multi-scale fusion MDSSD was put forward.On the basis of SSD network,MDSSD network used fusion module to implement the skip connections between deep and shallow networks on specific parts recognition scene,especially for the small target parts.What’s more,MSSD network can implement classification prediction and the regression of the coordinates.On the testing dataset of screws,nuts and gaskets in a specific environment,SSD network and MDSSD network were used to detect the single-class parts of a single image and the multi-class parts of a single image.Experiments showed that the mean average prediction of MDSSD algorithm was up to 97.11%,and the detection speed was up to 32.00 fps.Compared with the original SSD algorithm,the mean average prediction of MDSSD algorithm was up to 6.21%,which can detect parts in real time with high precision,meet the recognition needs of parts,and solve practical industrial problems.
关 键 词:SSD MDSSD 图像处理 多尺度融合 跨层连接 零件识别
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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