To evaluate the safety of construction site workers, deep learning models recognizing workers and safety equipment in construction site images are widely used. However, it is frequently observed that deep learning models based on supervised learning methods do not work well for unseen data in other domains having different visual characteristics. To address this issue, a novel method for generalizing semantic segmentation models was proposed. This method adopts two strategies: a domain adaptation method based on self-supervised learning and a copy-paste data augmentation. Source domain data with annotations (workers and hardhats) and target domain data without annotations are used for model training in a self-supervised learning scheme. The proposed model showed an improved generalization capability in semantic segmentation without annotation data of the target domain.
|Title of host publication||Proceedings of the 38th International Symposium on Automation and Robotics in Construction, ISARC 2021|
|Editors||Chen Feng, Thomas Linner, Ioannis Brilakis|
|Publisher||International Association for Automation and Robotics in Construction (IAARC)|
|Number of pages||6|
|Publication status||Published - 2021|
|Event||38th International Symposium on Automation and Robotics in Construction, ISARC 2021 - Dubai, United Arab Emirates|
Duration: 2021 Nov 2 → 2021 Nov 4
|Name||Proceedings of the International Symposium on Automation and Robotics in Construction|
|Conference||38th International Symposium on Automation and Robotics in Construction, ISARC 2021|
|Country/Territory||United Arab Emirates|
|Period||21/11/2 → 21/11/4|
Bibliographical noteFunding Information:
Project for Smart Construction Technology (No.21SMIP-A158708-02)” funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.
This research was conducted with the support of the “2021 Yonsei University Future-Leading Research Initiative (No.2021-22-0037)” and the “National R&D
© 2021 Proceedings of the International Symposium on Automation and Robotics in Construction. All rights reserved.
All Science Journal Classification (ASJC) codes
- Building and Construction
- Civil and Structural Engineering
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality
- Artificial Intelligence
- Computer Science Applications