# A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography
## 1 Introduction  
Slab tracks are widely used in newly built high-speed railways (HSRs) due to its significant advantages, such as high stability and low maintenance cost. However, under the joint effect of the large wheel-rail force induced by high-speed trains and the environmental factors in operation, slab tracks are prone to structural defects, such as surface cracks [1, 2]. To prevent these cracks, the design cracking joint (DCJ), as a structural optimization measure, is adopted in some prefabricated concrete slabs [3, 4]. However, according to the operation and maintenance practice in Chinese HSR, surface cracks can still occur in the non-DCJ areas on slabs [5]. More seriously, full cracks may develop, which is a hidden danger to the operational safety of HSRs. Although the maintenance specification of the Chinese HSR formulates standards for the inspection and maintenance of surface cracks, the timely detection of these cracks is challenging. This is mainly because the current methods of crack detection mainly depend on inefficient manual inspection within a narrow time window. Therefore, it is of great practical significance to develop an efficient surface-crack-detection method for HSR slab tracks. The current detection methods for surface cracks can be classified into two categories: contact methods and non-contact methods. The contact methods, as traditional inspection methods, usually involve the placement of sensors on the track-slab surface to detectcracks. The main examples of these methods are the impact-echo method [6; 7], the acoustic emission method [8; 9], and the optical-fiber-sensing method [10; 11]. These methods can accurately locate cracks and identify their sizes, but their detection efficiency is low and the requirement for operators is high. Therefore, these methods are applicable to the detection of key parts of slab tracks rather than the inspection of a whole line. In comparison, the non-contact methods are relatively new, based on the development of modern sensing technology. Characterized by high detection efficiency, these methods are increasingly applied in HSRs. In particular, the most efficient method is the use of the high-speed track-inspection train, whereby the service condition of the track structure, including some typical defects, can be detected at high speeds [12]. However, the inspection train cannot directly detect cracks on the surfaces of track slabs [13]. To solve this problem, some novel track-inspection trolleys equipped with ground-penetrating radar (GPR) [14], machine vision (with visible cameras) [15; 16], and infrared thermography (IRT) [5; 17; 18] devices have been developed in recent years. However, because of the influence of the steel bars inside the reinforced track slab, the accuracy of the geological radar-imaging method is low. For the machine-vision-based method, the dark condition during the window time at night can significantly limit its application in HSRs. In comparison, infrared thermography, which is used to measure the temperature fields of track slabs and does not need additional illuminating devices, is more suitable for detecting surface cracks in track slabs. The existing studies are mainly exploratory discussions about the feasibility of identifying surface cracks using aging methods. There is still a lack of studies on how to effectively extract the features of slab-surface cracks and assess these cracks in a quantitative manner. Therefore, this paper proposes a novel image-processing method, which can locate and quantify crack areas in track slabs using infrared thermography. Figure 1 illustrates the flowchart of the proposed method. The effectiveness of the proposed method at different temperatures is validated against a laboratory test, in which the temperature field of a scale model of a track slab with artificial cracks is studied. The remaining parts of this paper are as follows. The second part presents the image-enhancement and edge-detection methods in the processing of the infrared-thermography data.