Reinforced concrete bridge substructures are one of the most important road components, requiring routine maintenance for road safety. These structures initially require visual inspection to identify and prioritize maintenance processes based on the damage severity deteriorating the structural strength of the bridge. However, owing to the limitations concerning field inspector resources and budget management efficiency, automation technology is introduced to support field inspectors for developing faster defect detection and inspection processes with high accuracy on a large scale. It has advantages in utilizing the limited number of field inspectors more efficiently and preventing the development of defect severity, which requires more time and budget resources for maintenance. This paper describes the complete development of a deep learning-based visual defect-inspection system for reinforced concrete bridge substructures. The system consists of four main components. The first part involves the image acquisition. The second part aims to detect images with defects using a modified ResNet-50 CNN, improved from our previous research. The third part is defect classification, where different types of defects, such as cracking, erosion, honeycomb, scaling, and spalling, are classified using a previous modified ResNet-50. The last part considers the severity prediction using ANN. As a result, the inspection accuracy rate for defect detection, classification accuracy, and severity prediction are 90.4%, 81%, and 78%, respectively. The promising results of this study were acceptable by Thailand’s Department of Highways for practical use. Finally, this study could support Thailand’s Department of Highways and surrounding construction industries as a standard strategy toward digital transformation.
