Technological innovation in the field of roads is needed so that the process of detecting road damage can be carried out more quickly, precisely, and accurately. Real-time automation is required to increase efficiency, reduce costs, and improve the accuracy of road condition evaluation. This paper aims to explore the road damage detection model by determining and evaluating the appropriate category of road damage. The first step is to examine the hardware and software that can be used to create a road infrastructure condition assessment model. The categories of devices explored are related to the device's purchase price, technical specifications, implementation duration, and ease of use. The exploratory approach includes literature reviews, interviews, and focus group discussions to identify hardware and software that can be used. Several methods and technologies can be used to form a real-time road infrastructure condition assessment model. Based on the comparison of software and hardware, an Artificial Intelligence-based RoDDITS model was generated as a real-time detection of objects model for identifying, categorizing, and measuring diverse road damage kinds. Particularly concerning hole damage, additional work was conducted to derive a model for calculating the damage volume and facilitating 3D visualization. The RoDDITS model is produced using LiDAR + Low-cost GNSS Smartphone hardware and iRodd software.