###### Abstract
PAI offers a good contrast, relying on the optical principles (the imaging part) and yielding a good penetration utilizing the ultrasonic principles (the acoustic part) [11]. PAI provides a noncontact testing scheme and overcomes the limitations of inaccessibility issues (due to hazardous or other environmental/geometrical factors), coupling agent (water/gel) performance, and other such factors. The photoacoustic (PA) effect relies on the generation of acoustic waves by transferring heat energy from a laser source to a target region, resulting in an increase in the kinetic energy, causing a volumetric expansion of the target region. Figure 1a shows the block diagram for the major steps involved in a complete loop of a PAI-based system. Figure 1b presents a schematic diagram of a photoacoustic microscopy system utilized in this study. The ambient conditions again cause the target region to contract as soon as the laser source is turned off; this rapid volume expansion and contraction generates pressure waves (ultrasonic waves) in the target material, which travel through the material in all directions and carry important embedded information about the internal structure of the material under test. After propagating through the material, these ultrasonic waves are sensed at designated points, and the data are stored in a personal computer. Analysis of the acquired data yields important information about the internal structural details of the material. A variety of analyses techniques may be applied for the desired task, but with quick advances and substantial outcomes in the field of image processing, relying on machine learning/deep learning algorithms proves to be a good choice [12]. PAI has been widely used in biomedical applications for the detection of disease and abnormalities in the skin and vital organs, and for diagnosing cancers and eye diseases [13; 14]. However, in the field of NDT, there have been a few studies to detect the size and location of surface cracks. A study conducted by [PERSON] et al. showed that in the presence of cracks, the PA spectra appear to have mixed frequencies that may help in crack detection [15]. Another study utilizing metallic plates showed nonlinear PA imaging of the surface-breaking cracks [16]. [PERSON] et al. detected surface defects in metal plates employing an OR-PAM PAI setup [17]. Earlier, our group performed a numerical simulation-based study evaluating PAI capabilities for steel surface crack detection and mathematical modeling of its acoustic response [18]. The current study utilizes an actual PAI experimentation setup for PAI dataset construction, and then uses deep/machine learning-based techniques to estimate the size of surface cracks in steel samples. Machine learning (ML) models have proven their efficacy and strength for image processing tasks in the last few years [19]. ML models have been utilized in a multitude of image processing applications, with high performance and efficiency results. The ML models' applications in image processing range from object detection and pattern recognition to segmentation including healthcare [20; 21], optical microscopy [22], content-based image retrieval [23; 24], and much more. The detection of internal cracks is another crucial research avenue and requires an even more complex and rigorous methodology. The second section of this study demonstrates a numerical simulation-based feasibility study of the PAI technique for the detection of internal steel cracks. The acoustic responses to various internal defects with different sizes and locations are acquired. These responses are mutually compared statistically, and significance scores are calculated based on the analysis of variance (ANOVA) test. The ANOVA is a well-known predictor of analyzing group differences based on the signal mean and variances [25]. The contributions of this study are described as the construction of a dedicated PA image dataset for steel surface cracks using actual PAI experimentation.