Department of Computing - 2024/2025 Capstone Project
Project Code: MA2
Project Title:
Vision Based Framework for Automatic Interpretation, Classification and Detection of Construction Workers and Site Equipment
Objective of the Project:
The project, "Vision Based Framework for Automatic Interpretation, Classification and Detection of Construction Workers and Site Equipment," is an innovative initiative that aims to streamline construction site management using advanced computer vision techniques. The primary goal of this project is to develop a robust system capable of interpreting, classifying, and detecting construction workers and site equipment in real-time.
The system should utilize machine learning algorithms and computer vision to analyze video feeds from surveillance cameras installed at construction sites. It can accurately identify and classify various elements on a construction site, such as different types of equipment, vehicles, and workers. This information can be used to monitor site activities, track equipment usage, and ensure worker safety.
Furthermore, the system can detect any unusual activities or potential safety hazards, alerting site supervisors to take immediate action. This not only enhances operational efficiency but also significantly improves safety standards on construction sites.
This project represents a significant advancement in the field of construction site management, demonstrating the potential of artificial intelligence and computer vision in automating and optimizing complex processes. It should pave the way for a new era of smart, safe, and efficient construction practices.
Data Collection: Utilize a dataset containing images of construction workers and site equipment (or a dataset of your choice).
Algorithms: You should implement one or more algorithms of your choice, selecting one from each category and using the same dataset for all algorithms: ML Algorithm or DL Algorithm (Deel Learning is recommended)
Evaluation Metrics:
Algorithms should be compared based on various metrics, including but not limited to:
Accuracy: Proportion of correctly classified instances, Precision: Ratio of true positives to the total predicted positives, Recall: Ratio of true positives to the total actual positives, F1-Score: Harmonic mean of precision and recall, mAP: Mean Average Precision is used to analyze the performance of object detection and segmentation systems.
Visualization: Generate comparative graphs and tables to present results, facilitating easier analysis of the performance of each algorithm. This will include plots of accuracy across different algorithms.
Expected Outcome:
The report will culminate in a comprehensive report featuring detailed graphs and tables comparing the results of all implemented algorithms. To implement and process data using hardware and software implementation.
Submission: Please read the student handbook as a reference. The report should be the student handbook formatted and include elements such as graphs, flowcharts, equations, tables, and references. Ensure that all source code files, datasets, and relevant materials are uploaded to Blackboard. Your code/program must be normally executed using the OS you provided and include a README file that explains how to execute the code/program.
Knowledge/ Skill/ Tools Required:
Programming language (Python is Recommended), algorithm design (Machine Learning / Deep Learning is an added advantage)