## 1 Introduction
Hydrogen atoms exhibit a tendency to diffuse and accumulate around imperfections in the crystal lattice, leading to elevated hydrogen pressure [28; 29]. This process reduces the bond energy [30; 31] and aids in the accumulation of dislocations or localized plastic deformation [32; 33]. The main purpose of the inhibitor is to control hydrogen evolution and corrosion. This is performed when the reaction with the metal is taking place and the water decomposition process occurs [34; 35]. The inhibitor must assist in the recombination of hydrogen gas and inhibit the penetration of hydrogen into the metal lattice to avoid embrittlement [36]. The detection of hydrogen gas produced during the corrosion inhibition process could be used as an indication of inhibitor performance [37]. Standard electrochemical test methods such as potentiodynamic polarization (PP) can be applied to determine inhibitor effectiveness in corrosion inhibition tests [38]. To gain more insight into inhibitor effectiveness, a combination approach can be performed using the detection of hydrogen gas (H\({}_{2}\) bubbles) with these electrochemical methods and the bubble detection. This approach is able to provide more information on the effectiveness of an inhibitor. Image extraction of the corrosion process could be performed through a polarization test, based on video acquisition. Each image provides insight into the metal surface material transformation process and the H\({}_{2}\) bubbles' spread [34; 38]. Detecting the H\({}_{2}\) bubbles at a certain corrosion rate condition is not an easy job, for instance, in scenarios characterized by low current density [34; 39; 40]. To detect bubbles from a corrosion process in a metal weldment image, a machine learning algorithm (GLCM/SVM method) was implemented [6]. In the deep learning concept, the computer will attempt to map a feature from a set of images through a training process to acquire certain patterns, for example, object sizes, color channels, and so on, to support object detection schemes [41; 42]. Presently, a promising technology for object detection is YOLO (You Only Look Once), which has several parameters for justification, including the resulting model size, detection speed, and detection accuracy [43]. This algorithm is one of the popular object detections that has advantages in real-time performance and must be implemented with a training process on image data with the expected object class [44; 45]. The computation of these algorithms can be run by utilizing the computer's main processing tools or with the help of a graphics card to generate models for object detection. After the bubble is detected by the algorithm, the bubble can be quantified based on the weldment zones (weld metal, heat-affected zone, and base metal). Machine learning algorithms have been involved in the study of corrosion and inhibitors, such as quantum chemical variables [46], the molecular dynamics of the inhibitors [47], and measuring the immersion time and corrosion rate [48]. The purpose of this work is to detect bubbles produced from the corrosion inhibition process of a sodium molybdate on carbon steel based on H\({}_{2}\) evolution using a deep learning approach. The integration of conventional electrochemical testing and advanced deep learning algorithms in this research is a novel approach that has the potential to improve the field. This novel combination not only enhances the accuracy of the results but also opens up new avenues for further research and discovery. By calculating the corrosion rate based on PP tests on carbon steel specimens in 1 M HCl solution with up to 0.4 g/L sodium molybdate and detecting bubbles with a deep learning method on hydrogen gas bubble images captured during the electrochemical test, this work reveals some fundamental aspects of the inhibition mechanism and provides an example of the usefulness of deep learning in corrosion inhibitor research.