Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability
This paper presents the development of a novel real-time monitoring and detection system designed to identify the presence of welding helmets on workers’ faces during welding activities. The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10818429/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850285053640179712 |
|---|---|
| author | Mohammad Z. Shanti Chan Yeob Yeun Chung-Suk Cho Ernesto Damiani Tae-Yeon Kim |
| author_facet | Mohammad Z. Shanti Chan Yeob Yeun Chung-Suk Cho Ernesto Damiani Tae-Yeon Kim |
| author_sort | Mohammad Z. Shanti |
| collection | DOAJ |
| description | This paper presents the development of a novel real-time monitoring and detection system designed to identify the presence of welding helmets on workers’ faces during welding activities. The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained and validated using a diverse dataset that includes images with varying levels of blur, grayscale images, and drone-captured photos. The model’s effectiveness is evaluated using five key performance metrics: accuracy, precision, recall, F1 score, and the AUC-ROC curve. Additionally, the study investigates the impact of various input image sizes, batch sizes, activation functions, and the incorporation of additional convolutional layers on model performance. The results indicate that the Swish activation function, combined with a batch size of 128, an input image size of <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula>, and the addition of one convolutional layer, yielded superior performance. The model achieved outstanding values of 98% for precision, recall, and F1 score, along with an AUC of 0.98, underscoring its accuracy and reliability in detecting welding helmets. |
| format | Article |
| id | doaj-art-328a2be224bd478fbe0a95f7032d4ed4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-328a2be224bd478fbe0a95f7032d4ed42025-08-20T01:47:25ZengIEEEIEEE Access2169-35362025-01-01132187220210.1109/ACCESS.2024.352393610818429Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet AvailabilityMohammad Z. Shanti0https://orcid.org/0000-0002-6112-7967Chan Yeob Yeun1https://orcid.org/0000-0002-1398-952XChung-Suk Cho2Ernesto Damiani3https://orcid.org/0000-0002-9557-6496Tae-Yeon Kim4https://orcid.org/0000-0003-4743-6023Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Construction System Engineering, Korea Soongsil Cyber University, Seoul, South KoreaDepartment of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesDepartment of Civil and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesThis paper presents the development of a novel real-time monitoring and detection system designed to identify the presence of welding helmets on workers’ faces during welding activities. The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained and validated using a diverse dataset that includes images with varying levels of blur, grayscale images, and drone-captured photos. The model’s effectiveness is evaluated using five key performance metrics: accuracy, precision, recall, F1 score, and the AUC-ROC curve. Additionally, the study investigates the impact of various input image sizes, batch sizes, activation functions, and the incorporation of additional convolutional layers on model performance. The results indicate that the Swish activation function, combined with a batch size of 128, an input image size of <inline-formula> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula>, and the addition of one convolutional layer, yielded superior performance. The model achieved outstanding values of 98% for precision, recall, and F1 score, along with an AUC of 0.98, underscoring its accuracy and reliability in detecting welding helmets.https://ieeexplore.ieee.org/document/10818429/Deep learningindustrial accidentsconstruction safetyconvolution neural networkmachine learning |
| spellingShingle | Mohammad Z. Shanti Chan Yeob Yeun Chung-Suk Cho Ernesto Damiani Tae-Yeon Kim Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability IEEE Access Deep learning industrial accidents construction safety convolution neural network machine learning |
| title | Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability |
| title_full | Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability |
| title_fullStr | Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability |
| title_full_unstemmed | Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability |
| title_short | Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability |
| title_sort | automated welder safety assurance a yolov3 based approach for real time detection of welding helmet availability |
| topic | Deep learning industrial accidents construction safety convolution neural network machine learning |
| url | https://ieeexplore.ieee.org/document/10818429/ |
| work_keys_str_mv | AT mohammadzshanti automatedweldersafetyassuranceayolov3basedapproachforrealtimedetectionofweldinghelmetavailability AT chanyeobyeun automatedweldersafetyassuranceayolov3basedapproachforrealtimedetectionofweldinghelmetavailability AT chungsukcho automatedweldersafetyassuranceayolov3basedapproachforrealtimedetectionofweldinghelmetavailability AT ernestodamiani automatedweldersafetyassuranceayolov3basedapproachforrealtimedetectionofweldinghelmetavailability AT taeyeonkim automatedweldersafetyassuranceayolov3basedapproachforrealtimedetectionofweldinghelmetavailability |