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Welding Image Edge Detection and Identification Research Based on Canny Operator

机译:基于Canny算子的焊接图像边缘检测与识别研究。

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摘要

Edge is the most important feature of welding image. The classical algorithm of edge extraction is that the gray change of each pixel in neighborhood is considered and the change rule of first or second derivative in edge adjacent is used. In several edge detection operators commonly used, Lap lace operator often produces double-margin and others such as Sobel operator often form non-closed area. In this paper, Canny edge detection operator based on optimization algorithm is firstly discussed, which has advantages of large signal and noise ratio and high accuracy. Secondly, the welding image processing with Canny operator is described and for every step, detailed description and processing results are given. At last, Canny operator edge detection results are projected to 2-d histogram. The histograms of projection results are compared with the qualified image histogram. When the similarity is higher than some threshold, we think that the welding is qualified. Otherwise, it is considered as defective welding. Through the edge extraction and detection of lead-acid battery electrode welding that are done by welding robot, it can be seen that the recognition results meet the requirements of production line.
机译:边缘是焊接图像的最重要特征。边缘提取的经典算法是考虑邻域中每个像素的灰度变化,并使用边缘邻域中的一阶或二阶导数的变化规则。在几种常用的边缘检测算子中,Lap lace算子通常会产生双页边距,而其他诸如Sobel算子通常会形成非封闭区域。本文首先讨论了基于优化算法的Canny边缘检测算子,它具有信噪比大,精度高的优点。其次,描述了用Canny算子进行的焊接图像处理,并针对每个步骤给出了详细的描述和处理结果。最后,将Canny算子边缘检测结果投影到二维直方图。将投影结果的直方图与合格的图像直方图进行比较。当相似度高于某个阈值时,我们认为焊接合格。否则,将其视为焊接不良。通过焊接机器人对铅酸电池电极焊缝的边缘提取和检测,可以看出识别结果符合生产线的要求。

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