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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Adaptive Visual Inspection Method for Transparent Label Defect Detection of Curved Glass Bottle
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Adaptive Visual Inspection Method for Transparent Label Defect Detection of Curved Glass Bottle

机译：弯曲玻璃瓶透明标签缺陷检测的自适应视觉检测方法

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摘要

Automatic visual inspection of transparent materials has always been a challenging issue in industry due to complicated interference from reflection and refraction. In this paper, we present a study of machine vision system for automatic online inspection of transparent label defect on curved glass bottle. An area-array camera and a custom-made blue dome illumination device are introduced to capture high quality standstill image by eliminating reflection. To overcome the distortion issue on curved geometry shape, we have introduced the deformable template matching method for accurate location. An adaptive threshold selection strategy is proposed to effectively detect small scratch by using global and local threshold values together with Gaussian fitting algorithm. Considering the golden edge printing error, skeleton extraction and distance transformation are applied to detect the whole edge contour of Chinese characters with special font. Our visual inspection system has been deployed in a glass bottle manufacturing plant for on-line quality control. Field test result demonstrates that the detection accuracy reaches 99.5% at a speed of 60 pc/min for over 60,000 bottles.
机译：由于反射和折射的干扰复杂，自动目视检查透明材料始终是在工业中的具有挑战性的问题。本文介绍了弯曲玻璃瓶透明标签缺陷自动在线检查的机器视觉系统研究。引入区域阵列相机和定制的蓝色圆顶照明装置，通过消除反射来捕获高质量的静止图像。为了克服曲线几何形状的失真问题，我们引入了可变形的模板匹配方法，以便准确定位。提出了一种自适应阈值选择策略，以通过使用全局和局部阈值与高斯拟合算法一起有效地检测小划痕。考虑到金色边缘打印误差，应用骨架提取和距离变换，以检测具有特殊字体的汉字的整个边缘轮廓。我们的视觉检查系统已在玻璃瓶制造工厂部署，用于在线质量控制。现场测试结果表明，检测精度以超过60,000瓶速度为60 PC / min的速度达到99.5％。

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