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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques
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Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

机译：基于组合数字图像处理和深层学习技术的液化石油气压力容器焊接检查计算机视觉系统

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摘要

One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.
机译：压力容器的制造过程中最重要的操作之一是焊接。该操作的结果对船舶完整性产生了很大影响;因此，焊接检测程序必须检测可能导致事故的缺陷。本文介绍了一种基于结构光的计算机视觉系统，通过使用组合的数字图像处理和深层学习技术，基于结构光的焊接检查液化石油气（LPG）压力容器。在焊接操作之前应用的检查程序基于卷积神经网络（CNN），并且在方法测试期间，它正确地检测到在97.7％的情况下焊接的部件的未对准。焊后检测程序基于激光三角测量方法，估计焊珠高度和宽度，平均相对误差分别为2.7％和3.4％，在该方法测试期间分别为2.7％和3.4％。该焊接后检测程序使我们能够检测损害焊缝完整性的几何非圆形。通过使用该系统，在工业环境中实际验证期间，该过程的质量指标从95.0％提高到99.5％，证明其稳健性。

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