Integrated worker detection and tracking for the safe operation of construction machinery
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Son, H. | - |
dc.contributor.author | Kim, C. | - |
dc.date.accessioned | 2021-11-24T02:40:15Z | - |
dc.date.available | 2021-11-24T02:40:15Z | - |
dc.date.issued | 2021-06 | - |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.issn | 1872-7891 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51792 | - |
dc.description.abstract | Safety is the most important issue in the operation of machinery on a construction site. Due to the poor visibility of the surrounding environment, the machinery operated at construction sites poses a serious threat to the safety of the construction workers, as well as to the operators. This study proposes an integrated construction worker detection and tracking scheme using complementary metal-oxide semiconductor (CMOS) image sensors for real-time monitoring of the workspace and the safe operation of construction machinery. Various procedures were developed to detect and track construction workers in image sequences obtained from the CMOS image sensors. The architecture of the proposed scheme consists of the latest and fourth version of you only look once (YOLO) and the Siamese network, which are based on convolutional neural networks. Field experiments were performed to test the performance, while earthmoving operations were executed at the construction sites. The integrated architecture had recall, precision, and accuracy rates and F1 and F2 scores of 98.47%, 97.50%, 96.04%, 97.98%, and 98.27%, respectively. In addition, the results of worker detection and tracking were updated at 22 frames per second (fps). It is expected that the proposed method can be applied to operator assistance systems in construction machinery to achieve active safety. © 2021 Elsevier B.V. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Integrated worker detection and tracking for the safe operation of construction machinery | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.autcon.2021.103670 | - |
dc.identifier.bibliographicCitation | Automation in Construction, v.126 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000649684500004 | - |
dc.identifier.scopusid | 2-s2.0-85102743829 | - |
dc.citation.title | Automation in Construction | - |
dc.citation.volume | 126 | - |
dc.type.docType | Article | - |
dc.publisher.location | 네델란드 | - |
dc.subject.keywordAuthor | Active safety | - |
dc.subject.keywordAuthor | CMOS image sensor | - |
dc.subject.keywordAuthor | Construction machinery operation | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Integrated object detection and tracking | - |
dc.subject.keywordPlus | CMOS integrated circuits | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image sensors | - |
dc.subject.keywordPlus | Machinery | - |
dc.subject.keywordPlus | Metals | - |
dc.subject.keywordPlus | Network architecture | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Object detection | - |
dc.subject.keywordPlus | Oxide semiconductors | - |
dc.subject.keywordPlus | Active safety | - |
dc.subject.keywordPlus | Complementary metal-oxide semiconductor image sensor | - |
dc.subject.keywordPlus | Construction machinery | - |
dc.subject.keywordPlus | Construction machinery operation | - |
dc.subject.keywordPlus | Construction sites | - |
dc.subject.keywordPlus | Construction workers | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Detection and tracking | - |
dc.subject.keywordPlus | Integrated object detection and tracking | - |
dc.subject.keywordPlus | Workers' | - |
dc.subject.keywordPlus | MOS devices | - |
dc.relation.journalResearchArea | Construction & Building Technology | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Construction & Building Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.