## 2 Literature Review  
Conventional vibrothermography techniques rely on a high-power excitation source to introduce sufficient energy into the specimen [22]. Although effective in detecting the defect, high-energy excitation sources pose a significant risk, as they can potentially introduce additional damage to the material under inspection [23]. This limitation has spurred interest among researchers to develop an LVT technique as a safer alternative. [PERSON] et al. [24; 25] demonstrated that local defect resonance (LDR) can significantly enhance vibrothermography efficiency by producing noticeable temperature increases using low acoustic power input. However, one of the main challenges in applying LVT is the need for prior knowledge of the LDR frequency. To address this, [PERSON] et al. [25] developed a model that relates the LDR frequency to the defect geometry, material properties (Young's modulus, density, and Poisson's ratio), and the thickness of the material. Both the residual thickness at the defect and the overall plate thickness influence how the defect resonates. Meanwhile, [PERSON] et al. [26] used low-power ultrasonic excitation together with scanning laser vibrometry to detect LDR frequencies by analysing the vibrational response of composite specimens. [PERSON] et al. [27] demonstrated that micro-ultrasonic excitation thermography could successfully detect submillimetre porosities in CFRP composites, highlighting its complementary potential for low-power defect characterisation. [PERSON] et al. [28,29] introduced an alternative way to estimate the LDR frequency without relying on laser scanning vibrometry. Their method, known as Resonance Frequency Sweep Thermography (RFST), uses a wideband frequency sweep to excite a broad range of possible defect resonances. This approach simplifies the testing process and lowers equipment costs by removing the need to know the exact resonance frequency of a defect in advance. However, wideband sweep excitation often includes frequencies outside the operating range of the transducer, which leads to reduced heat generation and a lower SNR in thermal images [21]. Recent advancements in LVT have demonstrated its efficacy in detecting and quantifying various defects in CFRP composites, including corrosion, delamination, and BVID. [PERSON] et al. [22] developed a framework for detecting corrosion defects in CFRP-strengthened steel structures using LVT. By combining this technique with advanced image processing algorithms such as fast fourier transform (FFT) and partial least squares regression (PLSR), the authors significantly enhanced defect detectability and quantification. [PERSON] et al. [23] investigated the detection of artificial delaminations in CFRP composites using ultrasonic thermography. This study employed PCA, FFT, and TSR algorithms to process thermal image sequences, effectively improving SNR and enabling the quantification of delamination areas using [PERSON]'s thresholding method. In another study, [PERSON] et al. [30] explored the application of low-power ultrasonic thermography combined with manifold learning for the detection of BVID in CFRP specimens. The manifold learning algorithm extracted intrinsic features from thermal image sequences, effectively isolating damage information and suppressing thermal noise. This method demonstrated its capability to enhance sensitivity and reliability in detecting BVID. These studies highlight the critical role of integrating LVT with advanced image processing techniques, such as PCA, FFT, TSR, and manifold learning, to enhance defect visualisation and quantification. Compared to other active thermography methods, such as PT, significant research has been dedicated to optimising the processing of thermal image sequences. The combination of low-power excitation and sophisticated data analysis presents a promising pathway for improving the accuracy and efficiency of CFRP defect detection, paving the way for more reliable and effective NDT methods.