Weld seam recognition method based on fully convolutional neural network
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摘要: 在机器人自动化焊接中，精准高效的焊缝识别是实现高质量焊接的关键。针对现有视觉检测方法效率低、精度差的问题，提出了一种基于全卷积神经网络的焊缝识别方法。该方法首先采集数据对全卷积神经网络进行训练，得到最佳的网络参数；然后采用训练好的全卷积神经网络和最佳网络参数对焊缝图片进行语义分割，将焊缝所在区域与背景进行分离；然后对分割出的焊缝区域，进行骨架提取，得到接近单像素宽度的焊缝；之后根据自定义的直线度参数对焊缝形状进行判定，确定该焊缝是否为直线，用最小二乘法进行直线或曲线拟合，得到最终的焊缝轨迹。实验结果表明，所提方法能够快速准确地识别出焊缝位置和形状，可以作为自动焊接机器人轨迹自主规划和控制的技术基础。Abstract: In robot automatic welding, accurate and efficient weld identification is the key to achieve high quality welding. Aiming at the problems of low efficiency and poor accuracy of the existing visual inspection methods, a welding seam recognition method based on full convolutional neural network was proposed. Firstly, the method collects data to train the full convolutional neural network and obtains the best network parameters. Then, the trained fully convolutional neural network and the best network parameters were used to semantically segment the weld images, and the weld area was separated from the background. Then, the skeleton of the segmented weld area was extracted to obtain the weld with a width close to a single pixel. Then, the shape of the weld is determined according to the self-defined straightness parameters to determine whether the weld is a straight line, and the least square method is used to carry out the line or curve fitting to get the final weld trajectory. The experimental results show that the proposed method can quickly and accurately identify the weld position and shape, which can be used as the technical basis for the automatic welding robot trajectory planning and control.
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表 3 程序运行时间统计
区域分割
时间/s骨架提取
时间/s轨迹拟合
时间/s总时间/s 第一次 19.247 25.818 19.295 64.360 第二次 18.758 26.072 19.139 63.969 第三次 18.654 25.901 19.175 63.730 第四次 18.725 25.974 19.273 63.972 第五次 18.827 25.850 19.116 63.793 平均值 18.842 25.923 19.120 63.965 
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