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基于结构光的焊点智能识别算法设计
朱齐丹, 王彦柯, 朱伟, 刘玥
哈尔滨工程大学 自动化学院智能控制研究所, 哈尔滨 150001
摘要:
在自动焊接系统中,焊点的识别需要利用辅助激光,但是由于弧光的存在,而且一些金属材料具有反光性,这都会对辅助光的提取造成困难,因而影响到焊点的准确定位.基于此问题,利用反卷积结合特征金字塔网络,提出了基于热力图的焊点识别网络,该网络通过残差卷积神经网络进行提取特征,并利用金字塔策略将不同尺度的特征映射成特征点热力图,根据热力图得到焊点的最终准确位置.最后进行与模版匹配及原始的特征金字塔网络的对比试验.结果表明,该网络在对焊点的识别中比前两者的表现突出,而且鲁棒性较强,对于各种噪声和复杂的干扰具有很强的抵抗力.
关键词:  结构光  残差卷积神经网络  特征金字塔网络  热力图  焊点识别
DOI:10.12073/j.hjxb.2019400186
分类号:TG456.7;TP391.41
基金项目:国家自然科技基金资助项目(61673129)
Intelligent recognition algorithm of welding point based on structured light
ZHU Qidan, WANG Yanke, ZHU Wei, LIU Yue
Institute of Intelligent Control, College of Automation, Harbin Engineering University, Harbin 150001, China
Abstract:
In the process of automatic welding, welding point needs to be recognized with the help of laser. However, it suffers from the arc light and reflect light on the surface of some materials and the resulting accuracy of recognition cannot be guaranteed. In terms of this issue, the recognition network based on heatmap is proposed with combination of deconvolution and feature pyramid network. It extracts pyramid feature using residual convolutional neural network and generates key-point heatmap for each scale, which can tell the exact position of welding point. Compared with template matching and original feature pyramid network, such network performs better in the recognition of welding point with strong robustness and can work well in the context of various noise and complex interference.
Key words:  structured light  residual convolutional neural network  feature pyramid network  heatmap  recognition of welding point