Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures
Monitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accur...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/5/2320 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850228282695352320 |
|---|---|
| author | Ikchul Eum Jaejun Kim Seunghyeon Wang Juhyung Kim |
| author_facet | Ikchul Eum Jaejun Kim Seunghyeon Wang Juhyung Kim |
| author_sort | Ikchul Eum |
| collection | DOAJ |
| description | Monitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accuracy and speed, their findings often lack generalizability, partly due to inconsistent datasets and the need for more advanced techniques. In response, this study proposes an enhanced object detection method that integrates transformer-based backbone networks into the You Only Look Once (YOLO-version 10) framework. Evaluations conducted on a large-scale dataset of construction-site images demonstrate notable improvements in detecting the heavy equipment of varying sizes. Comparisons with other detectors confirm that the proposed model not only achieves higher accuracy but also maintains competitive processing speed, making it suitable for real-time deployment. Additionally, the dataset is made available for broader experimentation and development. These findings underscore the method’s potential to strengthen on-site safety by providing more reliable and efficient heavy equipment detection in complex work environments, while also acknowledging areas for further refinement. |
| format | Article |
| id | doaj-art-d1aa4e14428e4aeb8a3d55d2c3a236b6 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d1aa4e14428e4aeb8a3d55d2c3a236b62025-08-20T02:04:34ZengMDPI AGApplied Sciences2076-34172025-02-01155232010.3390/app15052320Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer ArchitecturesIkchul Eum0Jaejun Kim1Seunghyeon Wang2Juhyung Kim3Department of Architectural Engineering, Hanyang University, Seoul 133791, Republic of KoreaDepartment of Architectural Engineering, Hanyang University, Seoul 133791, Republic of KoreaDepartment of Architectural Engineering, Hanyang University, Seoul 133791, Republic of KoreaDepartment of Architectural Engineering, Hanyang University, Seoul 133791, Republic of KoreaMonitoring heavy equipment in real time is crucial for ensuring safety and operational efficiency at construction sites, yet achieving both high detection accuracy and fast inference remains challenging under diverse environmental conditions. Although previous studies have attempted to improve accuracy and speed, their findings often lack generalizability, partly due to inconsistent datasets and the need for more advanced techniques. In response, this study proposes an enhanced object detection method that integrates transformer-based backbone networks into the You Only Look Once (YOLO-version 10) framework. Evaluations conducted on a large-scale dataset of construction-site images demonstrate notable improvements in detecting the heavy equipment of varying sizes. Comparisons with other detectors confirm that the proposed model not only achieves higher accuracy but also maintains competitive processing speed, making it suitable for real-time deployment. Additionally, the dataset is made available for broader experimentation and development. These findings underscore the method’s potential to strengthen on-site safety by providing more reliable and efficient heavy equipment detection in complex work environments, while also acknowledging areas for further refinement.https://www.mdpi.com/2076-3417/15/5/2320heavy equipment detectionconstruction managementdeep learningobject detectionYOLOtransformers |
| spellingShingle | Ikchul Eum Jaejun Kim Seunghyeon Wang Juhyung Kim Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures Applied Sciences heavy equipment detection construction management deep learning object detection YOLO transformers |
| title | Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures |
| title_full | Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures |
| title_fullStr | Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures |
| title_full_unstemmed | Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures |
| title_short | Heavy Equipment Detection on Construction Sites Using You Only Look Once (YOLO-Version 10) with Transformer Architectures |
| title_sort | heavy equipment detection on construction sites using you only look once yolo version 10 with transformer architectures |
| topic | heavy equipment detection construction management deep learning object detection YOLO transformers |
| url | https://www.mdpi.com/2076-3417/15/5/2320 |
| work_keys_str_mv | AT ikchuleum heavyequipmentdetectiononconstructionsitesusingyouonlylookonceyoloversion10withtransformerarchitectures AT jaejunkim heavyequipmentdetectiononconstructionsitesusingyouonlylookonceyoloversion10withtransformerarchitectures AT seunghyeonwang heavyequipmentdetectiononconstructionsitesusingyouonlylookonceyoloversion10withtransformerarchitectures AT juhyungkim heavyequipmentdetectiononconstructionsitesusingyouonlylookonceyoloversion10withtransformerarchitectures |