Integrated worker detection and tracking for the safe operation of construction machinery
- Authors
- Son, H.; Kim, C.
- Issue Date
- Jun-2021
- Publisher
- Elsevier B.V.
- Keywords
- Active safety; CMOS image sensor; Construction machinery operation; Deep learning; Integrated object detection and tracking
- Citation
- Automation in Construction, v.126
- Journal Title
- Automation in Construction
- Volume
- 126
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51792
- DOI
- 10.1016/j.autcon.2021.103670
- ISSN
- 0926-5805
1872-7891
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/51792)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.