## 1 Introduction  
In several industrial sectors (e.g., aerospace sector), composite materials are replacing traditional metals and alloys due to their advantageous properties such as a high stiffness-to-weight ratio and a good corrosion resistance. On the other hand, the layered structure of composites makes them susceptible to internal damage features that may arise during production or during their service life. The presence of these defects may have a detrimental effect on the load-bearing capabilities of the component and must therefore be detected. t Flash thermography (FT) is a quick, full-field and non-contact non-destructive testing (NDT) technique that detects defects by exploiting the mismatch in thermal properties between the base material and an internal defect [1; 2; 3; 4]. While there exists alternative inspection schemes for infrared thermography, such as lock-in thermography [1; 5; 6] and frequency-(and/or phase-) modulated thermography [7; 8; 9; 10; 11], the focus of this paper lies on flash thermographic inspection. In FT, a short but intense flash excitation is used to introduce a heat flux on the sample's surface, which drives the diffusion of thermal waves in the material's through-thickness direction. Meanwhile, the sample's surface temperature evolution is recorded using a high-sensitivity infrared (IR) camera. At an internal boundary or defect, the thermal diffusivity mismatch between the defect and the sound material leads to a local abnormal temperature evolution. As such, a defect can be revealed as a localized anomaly in the recorded surface temperature profile. Unfortunately, thermographic inspection is inherently limited due to the strongly damped and highly diffusive nature of the induced thermal waves, making the detection of deep and small defects a challenging task. It is known that there exists a limiting depth beyond which defects cannot be detected, which depends on the thermal diffusivity, the defect depth, the recording duration, and the measurement's noise level [12; 13]. The defect detectability is significantly improved by post-processing of the recorded thermal data sequence using image processing [14; 15; 16] and data processing [2; 3; 17; 18; 19; 20] techniques. Some of the data processing algorithms operate directly on the time-domain data (i.e., the recorded thermal sequence), such as dynamic thermal tomography (DTT) [21; 22], differential absolute contrast (DAC) [23], and thermal signal area (TSA) [24]. On the other hand, performing the analysis in the frequency domain (i.e., after performing a fast Fourier transform) exhibits the advantageous property that undesirable effects of non-uniform heating and non-uniform surface emissivity are strongly reduced. Some of the analysis techniques operating in frequency domain are pulsed phase thermography (PPT) [18; 25; 26], modified pulse-phase thermography (PPT1/PPT2) [27], phase-domain thermal tomography (PDTT) [21], and adaptive spectral band integration (ASBI) [19]. PDTT may be considered as a phase-domain equivalent of DTT, where a pixel's phase angle instead of an indicative time provides information on the defect's depth [21]. PDTT, however, requires the operator to select both the evaluation frequency and the phase range of interest, and can scarcely be used for quantitative defect evaluation. The thermographic data may also be processed in different ways (e.g., in a statistical manner), as is done in principal component thermography (PCT) [28; 29], higher-order statistics (HOS) [30], and thermographic signal reconstruction (TSR) [26; 31], for example. In this paper, we present an experimental study on the efficiency of several time- and frequency-domain analysis techniques to obtain good defect detectability on several carbon fiber reinforced polymer (CFRP) samples with different defect types, sizes, and depths. A distinction is made between techniques that consider a single time (or frequency) bin and techniques that exploit an integration procedure over a specific time (or frequency) range.