Project Details
Drone Wall Crack Detection System
This project presents a drone-based infrastructure inspection system that leverages advanced machine learning algorithms to detect cracks in structures. Utilizing highresolution imaging and automated flight paths, the drone captures detailed images of infrastructure elements such as bridges, buildings, and roads. These images are processed using machine learning techniques to identify and classify cracks, enabling early detection of potential structural issues. The results are transmitted to a remote desktop application, providing real-time analysis and visualization of the findings. This system aims to enhance inspection efficiency, reduce human error, and improve safety in infrastructure maintenance, offering a scalable solution for civil engineering applications.
A drone-based infrastructure inspection system is developed using machine learning to detect structural cracks. A camera-equipped drone captures images that are analyzed by a CNN model to identify defects accurately. The approach improves inspection efficiency, reduces time, and enhances safety by limiting manual involvement.
The primary objective is to design and develop a drone-based infrastructure inspection system that can automatically detect structural cracks using machine learning techniques, reducing dependence on traditional manual inspection methods. Another objective is to enable high-resolution image acquisition of structures, including inaccessible and hazardous areas, through a camera-equipped drone, and to analyze these images using a trained convolutional neural network (CNN) for accurate crack detection. The project further aims to enhance inspection accuracy and efficiency while minimizing inspection time and human risk, thereby providing a safer, faster, and more reliable solution for structural health monitoring.
- Drone Type: Self-Designed Quadcopter
- Camera: 2 MP High-Resolution Camera Module
- Processing Unit: Onboard Computer / Laptop for ML Processing
- Image Resolution: 256 × 256 Pixels (Preprocessed)
- Machine Learning Model: Convolutional Neural Network (CNN)
- Frameworks Used: PyTorch, OpenCV
- Dataset Size: ~40,000 Images (Cracked & Non-Cracked)
- Flight Capability: Access to Hard-to-Reach and Hazardous Areas
- Operation Mode: Real-Time Image Capture and Crack Detection
- Application: Automated Infrastructure Inspection & Monitoring