# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 1 Introduction  
Transportation infrastructure, particularly highway networks, has been rapidly developed to meet transportation demands and connect economic centers. [PERSON] et al. [1] reported that while construction is a necessary condition, the sufficient conditions for any road to safely serve traffic participants include management, operation, and maintenance. Hence, researching and developing a precise automated inspection tool to support road maintenance agencies in effectively and swiftly addressing road deterioration/damage should be a top priority. For each road segment, [PERSON] et al. [2] emphasized that after construction and some years of use, various factors contribute to road damage, such as overloading, weather conditions, or low-quality materials. [PERSON] and [PERSON] [3] elaborated on the impact of road damage, leading to regrettable traffic accidents for traffic participants, reduced road operational efficiency, and ultimately increased road repair costs due to severe and unpreventable degradation. [PERSON] et al. [4] reported that timely and proper road maintenance can reduce costs by more than 20% compared to maintenance carried out after severe road damage occurs. Maintaining roads in optimal conditions is a significant challenge faced by road management agencies, and therefore, the development and application of modern technology platforms to support road maintenance agencies in efficiently and swiftly addressing road damage is a top research priority. In the context of road management, [PERSON] et al. [5] emphasized the significance of road defect detection forroad maintenance and management. [PERSON], [PERSON], [PERSON] and [PERSON] [2] stated that, to meet the demand for tools to assist in assessing road damage detection, multiple efforts have been made to evaluate the condition of asphalt roads. Various research studies have focused on road quality management models with different levels of comprehensiveness, such as manual assessments, the use of UAVs for damage detection, or laser scanning-based methods. [PERSON], [PERSON] and [PERSON] [1] also noted that manual road assessment is highly inefficient and susceptible to subjectivity and the biases of the technicians responsible for these inspections. Additionally, [PERSON] et al. [6] presented a technique related to the application of UAVs in transportation, specifically for traffic monitoring and highway infrastructure management. Although their research proves effective in identifying road damage, the results regarding damage localization are often neglected, and the technical demands of UAV technology are challenging for most countries to meet. Previous methods for road defect detection have relied on vibration sensors, accelerometers, or gyroscopes, as discussed by [PERSON] [7], [PERSON] and [PERSON] [8]. These methods have contributed to automating the detection of defects on road surfaces, but their installation has been cumbersome and prone to significant interference due to the vehicles' movement speed, leading to inefficiencies. Furthermore, laser scanning-based methods have been proposed for road surface damage detection by [PERSON] et al. [9]. Ground-based laser scanning methods aimed to expedite road condition assessments and make them safer and more systematic. However, these methods exhibited unstable results and low accuracy, and the continuous operation of laser scanning over several hours often resulted in decreased accuracy. Hence, the reliability of the task needs improvement, and the operational duration must be extended. Given these limitations, road management agencies and researchers have increasingly turned to automatic machine learning-based road damage/defect identification and classification techniques to address road management challenges effectively. In recent years, improvements in image processing techniques (IPT) have seen significant advancements in automated road surface crack detection models [10].