基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别  

Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding

作　　者：何银水[1,2] 孔萌 陈华斌[1] 陈玉喜[1] 陈善本[1] 

机构地区：[1]上海交通大学材料科学与工程学院,上海200240 [2]南昌大学资源环境与化工学院,南昌330031 [3]上海发那科机器人有限公司,上海201906

出　　处：《焊接学报》2015年第12期51-55,115,共5页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(61374071;51405298);上海市科委资助项目(11111100300);国家发改委资助项目(HT[2012]2144)

摘　　要：设计了一种视觉传感器同时采集熔池和焊缝轮廓,并提出一种基于视觉注意机制的机器人厚板焊接焊缝轮廓的提取方法.该方法以图像的亮度信息和方向信息作为初级视觉特征,并以焊缝图像作为亮度特征图,把对焊缝图像进行膨胀处理再进行Gabor滤波的结果作为方向特征图.对上述两种特征图进行显著性度量,然后对度量结果进行自适应特征融合获取综合显著图,并对其进行阈值分割和最近邻聚类.结果表明,最后在所有类别中以成员数最多的类所覆盖的区域作为最先被注意的区域,而其包含的数据即为焊缝轮廓信息.This paper presents a novel vision sensor to capture weld pools and weld seam profiles simultaneously in the same frame for implementing autonomous route planning in robotic thick plate welding. A method of extracting the weld seam profile based on visual attention mechanism from weld pool background is proposed here. In this method,brightness and direction are selected as primary visual characteristics,and the captured image is deemed as the brightness feature map while the direction feature map is acquired by Gabor filtering. Then,the above two feature maps are measured individually by the different methods to better highlight corresponding characteristics,and the two measured feature maps are integrated into a comprehensive saliency map by a self-judging algorithm. To extract the seam profile,threshold segmentation is applied to the comprehensive saliency map and followed by nearest neighbor clustering. The maximum cluster is the extracted seam profile and considered as the first noticed region. Experimental results show the effectiveness of the proposed method.

关 键 词：视觉注意 焊缝识别 机器人焊接 厚板焊接 

分 类 号：TG409[金属学及工艺—焊接]