Abstract:
This project develops a machine learning-based road damage reporting application that
allows users to report damage in real-time with photos and descriptions. Reports are forwarded to
authorities via a web-based administrative dashboard, enabling validation, categorization and
prioritization of corrective actions. The automatic notification feature keeps users informed about
report developments.
Testing is carried out to ensure a friendly user interface, cross-device compatibility, realtime response and an effective notification system. Evaluation includes compatibility with
TensorFlow Lite, camera quality, and internet connectivity. The results show the app works well
across a wide range of devices and network conditions, providing a consistent and responsive user
experience.
This project is expected to increase the efficiency and responsiveness of road maintenance
services and strengthen community involvement in reporting road damage. This application can
be a model for other infrastructure reporting systems.
