Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period
In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challe...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/574 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587511625416704 |
|---|---|
| author | Seonjun Yoon Hyunsoo Kim |
| author_facet | Seonjun Yoon Hyunsoo Kim |
| author_sort | Seonjun Yoon |
| collection | DOAJ |
| description | In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques. The system utilizes YOLOv5 for detecting jack supports in both construction drawings and on-site images captured through wearable cameras, while optical character recognition (OCR) and natural language processing (NLP) extract installation and dismantling timelines from work orders. The proposed framework enables continuous monitoring and ensures compliance with retention periods by aligning on-site data with documented requirements. The analysis includes 23 jack supports monitored daily over 28 days under varying environmental conditions, including lighting changes and structural configurations. The results demonstrate that the system achieves an average detection accuracy of 94.1%, effectively identifying discrepancies and reducing misclassifications caused by structural similarities and environmental variations. To further enhance detection reliability, methods such as color differentiation, construction plan overlays, and vertical segmentation were implemented, significantly improving performance. This study validates the effectiveness of integrating visual and textual data sources in dynamic construction environments. The study supports the development of automated monitoring systems by improving accuracy and safety measures while reducing manual intervention, offering practical insights for future construction site management. |
| format | Article |
| id | doaj-art-9633e2e23a964b618aaa8ab6ed9e3d69 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9633e2e23a964b618aaa8ab6ed9e3d692025-01-24T13:49:24ZengMDPI AGSensors1424-82202025-01-0125257410.3390/s25020574Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention PeriodSeonjun Yoon0Hyunsoo Kim1Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Republic of KoreaDepartment of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Republic of KoreaIn the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques. The system utilizes YOLOv5 for detecting jack supports in both construction drawings and on-site images captured through wearable cameras, while optical character recognition (OCR) and natural language processing (NLP) extract installation and dismantling timelines from work orders. The proposed framework enables continuous monitoring and ensures compliance with retention periods by aligning on-site data with documented requirements. The analysis includes 23 jack supports monitored daily over 28 days under varying environmental conditions, including lighting changes and structural configurations. The results demonstrate that the system achieves an average detection accuracy of 94.1%, effectively identifying discrepancies and reducing misclassifications caused by structural similarities and environmental variations. To further enhance detection reliability, methods such as color differentiation, construction plan overlays, and vertical segmentation were implemented, significantly improving performance. This study validates the effectiveness of integrating visual and textual data sources in dynamic construction environments. The study supports the development of automated monitoring systems by improving accuracy and safety measures while reducing manual intervention, offering practical insights for future construction site management.https://www.mdpi.com/1424-8220/25/2/574jack supportmonitoringdocument informationobject detectionoptical character recognition (OCR)natural language processing (NLP) |
| spellingShingle | Seonjun Yoon Hyunsoo Kim Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period Sensors jack support monitoring document information object detection optical character recognition (OCR) natural language processing (NLP) |
| title | Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period |
| title_full | Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period |
| title_fullStr | Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period |
| title_full_unstemmed | Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period |
| title_short | Time-Series Image-Based Automated Monitoring Framework for Visible Facilities: Focusing on Installation and Retention Period |
| title_sort | time series image based automated monitoring framework for visible facilities focusing on installation and retention period |
| topic | jack support monitoring document information object detection optical character recognition (OCR) natural language processing (NLP) |
| url | https://www.mdpi.com/1424-8220/25/2/574 |
| work_keys_str_mv | AT seonjunyoon timeseriesimagebasedautomatedmonitoringframeworkforvisiblefacilitiesfocusingoninstallationandretentionperiod AT hyunsookim timeseriesimagebasedautomatedmonitoringframeworkforvisiblefacilitiesfocusingoninstallationandretentionperiod |