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Enhancing Automated Defect Detection in Collagen Based Manufacturing by Employing a Smart Machine Vision Technique

机译:通过采用智能机器视觉技术提高基于胶原蛋白的自动缺陷检测

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Machine vision is now being extensively used for defect detection in the manufacturing process of collagen-based products such as sausage skins. At present the industry standard is to use a LabView software environment to manage and detect any defects in the collagen skins. Available data corroborates that this method allows for false positives to appear in the results which is responsible for reducing the overall system performance and resulting wastage of resources. Hence novel criteria were added to enhance the current techniques. The proposed improvements aim to achieve a higher accuracy and flexibility in detecting both true and false positives by utilizing a function that probes for the color deviation and fluctuation in the collagen skins. After implementation of the method in a well-known Australian company, investigational results demonstrate an average 26% increase in the ability to detect false positives with a corresponding substantial reduction in operating cost.
机译:机器视觉现在广泛用于胶原蛋白的产品的制造过程中的缺陷检测,例如香肠皮。目前行业标准是使用LabVIEW软件环境来管理和检测胶原皮肤中的任何缺陷。可用的数据证实了此方法允许误报出现在结果中,这些结果负责降低整体系统性能并导致资源的浪费。因此,添加了新颖的标准以增强目前的技术。拟议的改进旨在通过利用探针对胶原皮肤的颜色偏差和波动的函数来检测真实和误报的更高的准确性和灵活性。在澳大利亚着名的公司实施该方法后,调查结果展示了在经营成本相应大幅减少的误报方面的平均增加26%。

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