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The Defect Detection Algorithm for Tire X-Ray Images Based on Deep Learning

机译:基于深度学习的轮胎X射线图像缺陷检测算法

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For current domestic and international tire detection systems, the software operation of them is complex and poor to be put into application. In reality, it is necessary to do the task of defect detection by observing the X-ray image of the tire with the help of human eyes. This practice is affected by some subjective factors and both the accuracy and efficiency vary from person to person without strong robustness. To tackle this issue, one detection algorithm for tire defects based on deep learning is proposed. In this case, the model is trained, learnt and tested using the collected defect samples preprocessed from tire X-ray images. The designed algorithm was verified by the developed automatic tire defect detection software, in which the desired results were obtained.
机译:对于当前的国内和国际轮胎检测系统,它们的软件运行是复杂且穷人的应用。实际上,通过在人眼的帮助下观察轮胎的X射线图像,有必要通过观察轮胎的X射线图像来完成缺陷检测的任务。这种做法受到一些主观因素的影响,准确性和效率都因人员而没有强大的鲁棒性而变化。为了解决这个问题,提出了一种基于深度学习的轮胎缺陷的一种检测算法。在这种情况下,使用预处理的轮胎X射线图像预处理的收集的缺陷样本进行培训,学习和测试模型。通过开发的自动轮胎缺陷检测软件验证了设计的算法,其中获得了所需的结果。

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