# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 1 Introduction
These models involve precise and rapid analysis of two-dimensional (2D) road surface images to extract and assess relevant features for detecting the presence of cracks and tagging appropriate images for further analysis. Pioneering models in this area were proposed by [11; 12]. However, it was noted that the results of these machine learning models were negatively impacted by incorrect feature extraction, often due to manual extraction. According to [PERSON] et al. [13], recent models based on deep learning have been identified as the most effective method for detecting objects like cracks in concrete structures and road damage. When considering road damage detection models based on machine learning techniques, several other notable studies have been proposed. These include road damage detection using Fully Convolutional Neural Networks (FCNN) or methods for detecting potholes based on videos recorded with smartphones and integrated diagnostics, reported [PERSON] and [PERSON] [14]. However, these methods have seen limited practical application due to their theoretical limitations and lack of practicality. [PERSON] et al. [15] proposed a new neural network structure as well as training and prediction methods. [PERSON] et al. [16] and [PERSON] et al. [17] conducted research and proposed a new deep neural network algorithm to detect road damage conditions, aiming to establish a safe road environment. Their study focused on images containing various types of road damage. Indeed, despite the improvements, crack detection based on image processing can still be prone to errors due to various factors, including poor road surface lighting and non-uniform pavement material structures. [PERSON] et al. [18] report highlights that although these proposed algorithms have been compared to deep learning models, it seems that this tool has not been widely adopted by traffic management agencies due to its theoretical nature and lack of practicality. As reported by [PERSON] et al. [19], research directions in improving the management, operation, and maintenance of transportation infrastructure are currently focusing on enhancing productivity, quality, efficiency, and cost-effectiveness. Applying the scientific achievements of the Fourth Industrial Revolution to the management, operation, and maintenance of transportation infrastructure is an open and appropriate approach aligned with the current infrastructure and future trends, as reported by [PERSON] et al. [20]. In 2020, [PERSON], [PERSON], [PERSON] and [PERSON] [2] reported an important study on the performance of deep learning models for road damage detection using various dashcam image resources. This research is significant not only in advancing machine learning-based detection models but also in evaluating the performance of each model to compare their effectiveness. However, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [17] also reported that software developers have been working on developing and updating upgraded versions of previous software. These subsequent versions not only enhance performance but also introduce many new features. [PERSON], [PERSON], [PERSON] and [PERSON] [2] noted that for technology developers, research on product upgrades and improvements is a survival race, for example, with technologies like CNN, R-CNN, and Fast R-CNN. In recent years, Yolo V5 has emerged as an efficient and practical software for infrastructure object detection [21]. Therefore, this research applied the Yolo V5 model to develop a comprehensive software called RTI-IMS, which can automatically and accurately detect road surface damage. This study incorporates numerous previous achievements of scientists worldwide while eliminating relative limitations to create a more refined software. The RTI IMS software was trained using a dataset of 9053 images published [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [16]. The research also developed a tripled variation using the Mosaic method to enhance object image recognition capabilities.