This paper focuses on the new model for the classification of railhead defects, through images acquired by a rail inspection vehicle. In this regard, we discuss the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed. The performance is based on the data set composed of images provided by a Brazilian railway company, which covers the four possible railhead defects (cracking, flaking, head-check and spalling) and the normal condition of the railhead. Additionally, we apply different levels of additive white Gaussian noise in the images in order to challenge the proposed model. Finally, we discuss performance analysis in terms of convergence speed, computational complexity reduction, and classification ratio. The reported results show that the proposal achieved improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required under real-time constraint or low computational resource availability.

本文将重点放在通过铁路检查车辆获取的图像对铁轨头缺陷进行分类的新模型上。在这方面，我们讨论了从自适应滤波器理论派生的集合成员概念在上下单例2型模糊逻辑系统的训练过程中的使用，目的是降低计算复杂度并提高收敛速度。性能基于由巴西铁路公司提供的图像组成的数据集，其中涵盖了四种可能的轨头缺陷（开裂，剥落，头部检查和剥落）以及轨头的正常状况。此外，我们在图像中应用了不同级别的加性高斯白噪声，以挑战所提出的模型。最后，我们从收敛速度的角度讨论性能分析，计算复杂度降低，分类率降低。报道的结果表明，当我们限制训练的时期数时，该提议实现了提高的收敛速度，略高的分类率和显着的计算复杂性降低，这在实时约束或低计算资源可用性下可能是必需的。