Please use this identifier to cite or link to this item:
Title: Visual recognition using artificial intelligence (safety detection in the workplace using artificial intelligence)
Authors: Liu, Guang Yuan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Liu, G. Y. (2022). Visual recognition using artificial intelligence (safety detection in the workplace using artificial intelligence). Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: A3300-211
Abstract: Numerous studies have concluded that non-compliance with personal protective equipment (PPE) requirements dramatically affects the workplace’s safety level. Though currently available strategies for detecting compliance of PPE requirements could provide rapid and fast detection of helmets, it lacks the capability of detecting other PPE such as vests, gloves, and masks. Furthermore, the lack of dynamic user interfaces further complicates the deployment and application of such techniques. Therefore, the objective of this project is to design a real-time PPE monitoring system that is both efficient and accurate. To achieve this, different you only look once (YOLO) models were tested and benchmarked against one another, and YOLOv5s was selected for its accuracy and detection speed. After selecting the model, multiple experiments such as hyperparameters fine-tuning, model structure modification and data augmentation were performed to increase detection accuracy further. Meanwhile, a novel dataset was constructed containing 3414 high-resolution images with 28,977 instances across 8 different classes. With the new dataset, the trained model obtained a 69.5% mean average precision at 32 frames per second. In addition, a flexible graphical user interface was developed to enable users to customise detection features as well as the camera source. Finally, a geofencing function was also implemented to allow users to customise the precise monitoring areas.
Schools: School of Electrical and Electronic Engineering 
Organisations: A*STAR
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
A3300-211 NTU EEE FYP_Report_LiuGuangyuan.pdf
  Restricted Access
3.47 MBAdobe PDFView/Open

Page view(s)

Updated on Dec 3, 2023

Download(s) 50

Updated on Dec 3, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.