Abstract
Tire modeling is a fundamental task that experts must carry out to ensure optimal tire performance in terms of stability, grip, and fuel consumption. In addition to the major forces that act on the tire, the temperature changes that occur during test handling provide meaningful information for an accurate model. However, the analysis of the temperature in a rolling tire is not a trivial task due to the interactions of the tire and the pavement. A non-invasive technique, such as thermal infrared inspection, allows analyzing temperature changes on the surface of the tire under dynamic rolling conditions. Thus, the accurate segmentation of the tire is the first objective towards a better understanding of its performance. To this aim, we propose a novel approach that combines image processing techniques with convolutional neural networks. First, the handcrafted features extracted from the infrared images are used to build a dataset; then, a convolutional neural network is trained with the labeled images. Finally, the network makes predictions of the tire surface under different test conditions. The results have shown that our proposal achieves a segmentation accuracy \({>}\)0.98 and a validation error \({<}\)0.05.
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Nava, R., Fehr, D., Petry, F., Tamisier, T. (2021). Tire Surface Segmentation in Infrared Imaging with Convolutional Neural Networks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_5
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DOI: https://doi.org/10.1007/978-3-030-68821-9_5
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