AI Road Damage Detection System (Academic Project)

Project Domain / Category Image Processing – Deep Learning / Computer Vision / Web Application Abstract Road damage such as cracks is a common problem that can affect traffic safety, vehicle conditions, and road maintenance. Traditionally, road inspection is performed manually through physical surveys and visual inspection. These approaches can be time-consuming, costly, and difficult to perform regularly over large road networks. With advances in computer vision and deep learning, road images and video can be analyzed automatically to identify visible road damage. This project aims to develop an AI-Based Road Damage Detection and Monitoring System using deep learning techniques. The proposed system will analyze uploaded road images or real-time video streams to detect and classify road damage such as potholes and cracks. A deep learning model will be trained using a dataset containing road images with different types of damage. The model will be integrated with a web-based interface where users can upload images or provide a live camera feed and view the detected damage with visual indicators. The system can also maintain detection records and generate reports for monitoring and maintenance purposes. The proposed technology can support road inspection, transportation management, and timely maintenance planning. Functional Requirements 1. User Management & Authentication Manages user registration, login, and access control for authorized system usage. 2. Image & Video Input Allows users to upload road images or provide video/camera input for automated road damage analysis. 3. Road Damage Detection Engine (AI-Based) Uses a deep learning model to automatically detect visible road damage from images and video. 4. Crack Detection Module Detects visible road cracks using image processing and deep learning techniques. 5. Damage Classification Module Classifies detected road damage into predefined categories such as potholes and cracks. 6. Detection Visualization Displays detected damage using bounding boxes, labels, confidence scores, or other visual indicators. 7. Recording & Evidence Management Stores detected road-damage images, prediction results, timestamps, and related evidence for future reference. 8. Monitoring Dashboard Provides a user-friendly dashboard to display detection results, damage categories, severity information, and system status. 9. Report Generation & Analytics Generates reports and statistics based on detection history, damage types, severity, and system performance. 10.Database Integration Stores user information, detection results, timestamps, image paths, predictions, and related records using SQLite or MySQL. 11.Admin & Model Management Allows administrators to manage users, system settings, stored records, and deployed AI models. 12.Web-Based User Interface Provides a web-based interface for image uploads, video analysis, viewing detection results, and accessing monitoring reports. Tools and Technologies Required • Programming Language: Python • Deep Learning: TensorFlow / Keras or PyTorch • Object Detection / Computer Vision: YOLO, OpenCV, Pillow • Backend: Flask / Django • Frontend: HTML, CSS, JavaScript • Database: SQLite / MySQL • IDE: PyCharm, VS Code, or Jupyter Notebook Dataset • Public Road Damage Detection Dataset (RDD) or a similar publicly available road-damage dataset • Road images containing cracks, and other predefined damage categories • Custom road images (optional) for additional testing or fine-tuning • Database: SQLite / MySQL