Abstract

It is crucial to analyze the tire under dynamic conditions to observe a performance close to realistic situations. Particularly, the temperature generated by the interaction of tire and pavement can provide useful information and understanding into how tire components can be optimized. However, in such a situation, data generation and measurement are challenging tasks. High-resolution thermal infrared imaging is a non-contact technology that transforms radiation patterns into a visible image and allows measuring the temperature changes on the surface of the tire. Therefore, the first step towards a systematic analysis of the performance of the tire is to segment the surface. To this end, we present an approach that combines traditional image processing methods with convolutional neural networks. We further investigate transfer learning techniques to improve the prediction of the proposed model on a different dataset. Our ultimate goal is to implement a robust network to segment a broad variety of tires. A segmentation accuracy >0.97 and a validation error <0.06 were achieved with transfer learning. The results have shown that our network can be extended to segment new data accurately.

中文翻译：

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使用卷积神经网络和迁移学习进行红外成像中的轮胎表面分割

摘要

在动态条件下分析轮胎以观察接近现实情况的性能至关重要。特别是，轮胎和路面相互作用产生的温度可以提供有用的信息和了解如何优化轮胎组件。然而，在这种情况下，数据生成和测量是具有挑战性的任务。高分辨率热红外成像是一种非接触式技术，可将辐射模式转换为可见图像，并允许测量轮胎表面的温度变化。因此，对轮胎性能进行系统分析的第一步是分割表面。为此，我们提出了一种将传统图像处理方法与卷积神经网络相结合的方法。我们进一步研究了迁移学习技术，以改进对不同数据集的建议模型的预测。我们的最终目标是实施一个强大的网络来细分各种轮胎。通过迁移学习实现了 >0.97 的分割精度和 <0.06 的验证误差。结果表明，我们的网络可以扩展到准确分割新数据。