MATLAB-based AI system for automatic segmentation and labeling of construction elements in videos using deep learning.
I chose this project because automated segmentation and labeling of objects in construction videos can enhance quality assurance, defect detection, and real-time monitoring in infrastructure projects. Manual inspection methods are time-consuming, labor-intensive, and susceptible to human error, making AI-driven solutions essential for efficiency and accuracy. This project utilizes MATLAB’s Deep Learning and Computer Vision Toolboxes to train a YOLO-based object detection model that identifies pavement types, cracks, drainage systems, and interlock flooring in construction environments. The implementation of this technology supports BIM integration, predictive maintenance, and digital twin applications, ensuring sustainable and intelligent infrastructure management.
This MATLAB-based AI project leverages deep learning for automatic segmentation and labeling of construction elements in videos. It applies YOLO-based object detection to classify pavement types, cracks, drainage systems, and interlock flooring, enhancing quality control and infrastructure monitoring.
- Automated object segmentation and labeling using deep learning.
- Real-time detection of construction components in videos.
- MATLAB Deep Learning Toolbox integration.
- YOLO-based model for high-accuracy detection.
- Clone this repository:
git clone https://github.com/YOUR_USERNAME/AI_Segmentation_Construction_Videos.git cd AI_Segmentation_Construction_Videos
Due to GitHub’s file size limits, the following large files are hosted externally: https://drive.google.com/drive/folders/18DWjOKag7A9FZMqv9tKhJcwv6K2hnSp3?usp=drive_link