Cloud Integrated Deep Learning System for Real-Time Road Hazard Detection and Geo-Spatial Localization

Road transportation safety is critically affected by surface-level hazards such as potholes, cracks, uneven roads, and speed breakers, which often lead to accidents, vehicle damage, and traffic congestion. Conventional road inspection techniques rely on manual surveys or sensor-based systems that are labor-intensive, costly, and incapable of providing real-time, scalable monitoring. Although recent deep learning–based computer vision methods have improved automated hazard detection, most existing solutions depend on on-device computation, requiring high-end hardware and limiting large-scale deployment. This paper presents a cloud integrated deep learning system for real-time road hazard detection and geo-spatial localization, where all computationally intensive tasks are offloaded to the cloud. Road images captured using client devices are preprocessed with OpenCV and transmitted through a Flask-based REST API to an AWS EC2 cloud server, where a TensorFlow/Keras deep learning model classifies multiple road hazard categories. The detected hazards are geo-tagged using GPS coordinates and visualized on Google My Maps for intuitive and effective geo-spatial representation. By centralizing deep learning inference and data management in the cloud, the proposed system reduces local hardware dependency, improves scalability, supports multi-user accessibility, and provides a cost-effective solution for smart transportation systems and intelligent road infrastructure management.