A New Model to Distinguish Railhead Defects Based on Set-Membership Type-2 Fuzzy Logic System

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.

Automated summary

This paper discusses a new model for classification of railhead defects using images acquired by a rail inspection vehicle, incorporating set-membership concept to reduce computational complexity and increase convergence speed. Experimental results show that the proposed model achieved improved convergence speed, slightly higher classification ratio, and significant computational complexity reduction when limiting the number of training epochs.