# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 2 Literature Review
The goal of the reviewed works were to improve thermograms through introducing new IRT approaches by using new excitation techniques (e.g., induction heating and eddy current) or excitation form (e.g., short pulse, long pulse, and modulated pulse). Another avenue which has been followed by many studies [48; 49; 50] in IRT is to employ image processing techniques to enhance recorded thermograms. These techniques may be classified into basic (preprocessing) and advanced processing. The main goals of preprocessing methods are to prepare the thermal images for high level processing and enhance them with the help of temporal and/or spatial filtering techniques widely used in the field of visible image processing [51]. This paper does not discuss applications of this group of methods in IRT; interested readers may refer to [12] for a good summary. To address some of the PT issues such as limitation in detecting deep defects and sensitivity to optical surface disturbances, [PERSON] and [PERSON] [49] developed pulsed phase thermography (PPT). PPT is an extension to PT by using the idea of frequency analysis (Fourier transform) in a manner similar to LT. Experimental configuration of PPT is identical to that of the classical PT. One of the widely used image processing methods for the defect detection from the recorded thermograms (in cooling down phase) is absolute thermal contrast (ATC) [45; 48; 52; 53], which is computed by subtracting the temperatures of defective and nondefective areas. Different thermal contrast definitions such as running contrast, the normalized contrast, maximum contrast, and the peak slope contrast have been proposed; for a summary refer to [12; 35]. The performance of ATC is degraded by nonuniform heating at the surface. To address this problem, differential absolute contrast (DAC) methods [54; 55] were developed. A common method for defect detection in LT and PPT is absolute phase contrast (APC) [56]; it is defined similar to ATC. Since phase is less sensitive to nonuniform heating and the surface emissivity variations, defects can be observed more clearly in PPT and LT in comparison with standard PT [14]. Defect detection using methods such as ATC and APC is not easy and they are very sensitive to noise. [PERSON] [50] attempted to address this problem by adapting the idea of principal components analysis (PCA) [57] for thermography. In IRT, this method is generally known as principal components thermography (PCT). In Section 4.3, the analytical model of PCT is derived and discussed. PCT has gained an increasing popularity in IRT because of a good noise-rejection [50] and the fact that it facilitates the process of inspection (only by observing the first few principal components rather than the entire image sequence). This technique has been widely used for defect detection in concrete [58], dynamic thermal video analysis [59], detecting delamination defects in composites [60], and impact damage of CFRP [61], just to name a few. PCT performance is better than conventional PT, and as maintained by some studies [50] comparable to PPT. However, there are also some reports [62] about superiority of PPT over PCT. According to this study, PCT performed well in terms of maximum normalized contrast, but it was not very effective in detecting either small or deep flaws. Moreover, PCT, as with PPT, is a computationally intensive algorithm. The main goal of existing works in IRT-based NDT so far has been to identify defects (e.g., cracks, delaminations, and corrosion) inside the test object and to estimate the defect depth. But in our study, we are interested in detecting cavities in the soil around a culvert pipe rather than inside the pipe. To the best of our knowledge, no study has assessed the feasibility of IRT for this task. Therefore, this work aims to address this literature gap and provide a foundation for future work on this topic.