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Automatic inspection system of adhesive on vehicle windshield using computational vision

机译:基于计算视觉的车辆挡风玻璃胶粘剂自动检测系统

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摘要

Polyurethane-based adhesives are applied on the windshields of vehicles in the automotive industry to fix the windshield and seal the cabin. A failure in the adhesive bead could allow water to ingress between the windshield and the vehicle body. If not detected in the leak test, it can lead to high cost due to warranty repairs, inconvenience to customers and damage to the brand. Commercial solutions are available in the market to detect an interruption in the adhesive bead right after its application on the windshield, before it is fitted to the vehicle, but at high cost. This paper proposes an automatic inspection system based on computer vision, low-cost hardware, programming in Python language and making use of open-source libraries. A batch of defect-free windshields was inspected using the proposed inspection system. In the impossibility of obtaining defective parts for validation, windshield images were modified to simulate defects and the images were evaluated by the developed algorithm. The algorithm showed quite good results at the end, and we could establish the system's effectiveness at 100 for defect detection capability and 21 of false detections.
机译:聚氨酯基胶粘剂应用于汽车工业车辆的挡风玻璃上,用于固定挡风玻璃并密封驾驶室。胶珠失效可能会导致水进入挡风玻璃和车身之间。如果在泄漏测试中未检测到,可能会因保修维修、给客户带来不便和品牌受损而导致高成本。市场上有商业解决方案,可以在将胶珠涂在挡风玻璃上后立即检测其是否中断,然后再将其安装到车辆上,但成本很高。本文提出了一种基于计算机视觉、低成本硬件、Python语言编程和利用开源库的自动检测系统。使用建议的检测系统检查了一批无缺陷的挡风玻璃。在无法获得有缺陷的部件进行验证的情况下,对挡风玻璃图像进行了修改以模拟缺陷,并通过开发的算法对图像进行了评估。该算法最终显示出相当不错的结果,我们可以确定系统在缺陷检测能力方面的有效性为 100%,错误检测率为 21%。

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