Research Article
Bengar, J. Z., Gonzalez-Garcia, A., Villalonga, G., Raducanu, B., Aghdam, H. H., Mozerov, M., Lopez, A. M., and van de Weijer, J. (2019). Temporal coherence for active learning in videos. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (pp. 914–923). IEEE. https://doi.org/10.1109/ICCVW.2019.00120
10.1109/ICCVW.2019.00120Choi, J., Elezi, I., Lee, H. J., Farabet, C., and Alvarez, J. M. (2021). Active learning for deep object detection via probabilistic modeling. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 10244–10253). IEEE. https://doi.org/10.1109/ICCV48922.2021.01010
10.1109/ICCV48922.2021.01010Duan, R., Deng, H., Tian, M., Deng, Y., and Lin, J. (2022). SODA: A large-scale open site object detection dataset for deep learning in construction. Automation in Construction, 142, 104499. https://doi.org/10.1016/j.autcon.2022.104499
10.1016/j.autcon.2022.104499Hwang, J., Jeong, I., Kim, J., and Chi, S. (2025). Web-based multi-vision platform for earthwork productivity on construction sites using real-time model updating. Frontiers of Structural and Civil Engineering, 19(6), pp. 1021–1040. https://doi.org/10.1007/s11709-025-1197-0
10.1007/s11709-025-1197-0Jocher, G., Qiu, J., Liu, M., Lyu, S., Akyon, F. C., and Kalfaoglu, M. E. (2026). Ultralytics YOLO26: unified real-time end-to-end vision models. arXiv preprint arXiv:2606.03748.
Kao, C. C., Lee, T. Y., Sen, P., and Liu, M. Y. (2019). Localization-aware active learning for object detection. In Computer Vision – ACCV 2018 (LNCS Vol. 11364, pp. 506–522). Springer. https://doi.org/10.1007/978-3-030-20876-9_32
10.1007/978-3-030-20876-9_32Kim, D., Liu, M., Lee, S. H., and Kamat, V. R. (2019). Remote proximity monitoring between mobile construction resources using camera-mounted UAVs. Automation in Construction, 99, pp. 168–182. https://doi.org/10.1016/j.autcon.2018.12.014
10.1016/j.autcon.2018.12.014Kim, J., Hwang, J., Chi, S., and Seo, J. (2020). Towards database-free vision-based monitoring on construction sites: A deep active learning approach. Automation in Construction, 120, 103376. https://doi.org/10.1016/j.autcon.2020.103376
10.1016/j.autcon.2020.103376Mannem, K. R., Mengiste, E., Vardhan, D., de Soto, B. G., and Bacao, F. (2026). Construction site object detection with active transfer learning and weighted adaptive uncertainty-diversity sampling using a small imbalanced dataset. Automation in Construction, 183, 106819. https://doi.org/10.1016/j.autcon.2026.106819
10.1016/j.autcon.2026.106819Nath, N. D., and Behzadan, A. H. (2020). Deep convolutional networks for construction object detection under different visual conditions. Frontiers in Built Environment, 6, Article 97. https://doi.org/10.3389/fbuil.2020.00097
10.3389/fbuil.2020.00097National Information Society Agency. (2022). Construction site hazardous condition assessment data[Data set]. AI-Hub. https://aihub.or.kr/aihubdata/data/view.do?dataSetSn=71407
Schmidt, S., Rao, Q., Tatsch, J., and Knoll, A. (2020). Advanced active learning strategies for object detection. In 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 871–876). IEEE. https://doi.org/10.1109/IV47402.2020.9304565
10.1109/IV47402.2020.9304565Sener, O., and Savarese, S. (2018). Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations (ICLR).
Settles, B. (2009). Active learning literature survey(Computer Sciences Technical Report No. 1648). University of Wisconsin–Madison.
Xiao, B., and Kang, S. C. (2021). Development of an image data set of construction machines for deep learning object detection. Journal of Computing in Civil Engineering, 35(2), 05020005. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000945
10.1061/(ASCE)CP.1943-5487.0000945Xuehui, A., Li, Z., Zuguang, L., Chengzhi, W., Pengfei, L., and Zhiwei, L. (2021). Dataset and benchmark for detecting moving objects in construction sites. Automation in Construction, 122, 103482. https://doi.org/10.1016/j.autcon.2020.103482
10.1016/j.autcon.2020.103482Yoo, D., and Kweon, I. S. (2019). Learning loss for active learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 93–102). IEEE. https://doi.org/10.1109/CVPR.2019.00018
10.1109/CVPR.2019.00018- Publisher :Korean Society of Automation and Robotics in Construction
- Publisher(Ko) :(사)한국건설자동화·로보틱스학회
- Journal Title :Journal of Construction Automation and Robotics
- Journal Title(Ko) :건설자동화·로보틱스 논문집
- Volume : 5
- No :3
- Pages :8-16
- Received Date : 2026-09-16
- Revised Date : 2026-09-29
- Accepted Date : 2026-09-29
- DOI :https://doi.org/10.55785/JCAR.5.3.8


Journal of Construction Automation and Robotics




