Creation of a Web-Based Tool for the Visual Inspection of Building Equipment

Fire safety inspection is essential for protecting occupants from fire hazards. Traditional inspection methods often rely on subjective assessments, often leading to potential errors and inadequate maintenance. This study introduces a visual fire safety inspection tool that integrates self-trained Machine Learning (ML) services to enhance the accuracy and efficiency of documenting Fire Safety Equipment (FSE) using images. The ML services were incorporated into a web-based application built with the React framework, featuring a backend developed using FastAPI and MongoDB for efficient processing and scalability. The tool achieved a high mean average precision (mAP) over 80% on testing datasets. It offers a robust environment for fire safety experts to validate and compare models, also providing insights into the impact of active learning algorithms. Despite the tool’s high accuracy, challenges such as slow loading times and application freezing were identified, with proposed solutions focusing on optimizing backend and frontend processes. The integration of ML services demonstrates significant potential for improving fire safety inspections, with future work aimed at refining models, expanding real-time monitoring capabilities, and ensuring compatibility with Building Information Modeling (BIM) systems and conventional smartphones.