Open Access
Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
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Article Number | 01053 | |
Number of page(s) | 15 | |
DOI | https://doi.org/10.1051/epjconf/202532801053 | |
Published online | 18 June 2025 |
- Bakirci, M. (2024). Utilizing YOLOv8 for enhanced traffic monitoring in intelligent transportation systems (ITS) applications. Digital Signal Processing, 104594. https://doi.org/10.1016/j.dsp.2024.104594 [CrossRef] [Google Scholar]
- Küpers, X. (2024). Idle identification of construction machinery through a deep learning-based algorithm embedded in surveillance camera systems [Master's thesis, University of Twente]. [Google Scholar]
- Lee, J.H., & You, S.J. (2024). Balancing privacy and accuracy: Exploring the impact of data anonymization on deep learning models in computer vision. IEEE Access, 12(1), 8346-8358. https://doi.org/10.1109/access.2024.3352146 [CrossRef] [Google Scholar]
- Onososen, A.O., Musonda, I., Onatayo, D., Saka, A.B., Adekunle, S.A., & Onatayo, E. (2024). Drowsiness detection of construction workers: A proactive approach to accident prevention leveraging YOLOv8 deep learning and computer vision techniques. Preprints. [Google Scholar]
- Zhou, Y., Yi, J., Xie, G., Jia, Y., Xu, G., & Sun, M. (2022). Human detection algorithm based on improved YOLO v4. Information Technology and Control, 51(3), 485-498. https://doi.org/10.5755/j01.itc.51.3.28057 [CrossRef] [Google Scholar]
- Zhang, S., Li, S., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022). Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, 22(4), 1476. https://doi.org/10.3390/s22041476 [CrossRef] [PubMed] [Google Scholar]
- Khan, I.U., Afzal, S., & Lee, J.W. (2022). Human activity recognition via hybrid deep learning-based model. Sensors, 22(323). https://doi.org/10.3390/s22010323 [PubMed] [Google Scholar]
- Kim, I.-S., Latif, K., Kim, J., Sharafat, A., Lee, D.-E., & Seo, J. (2022). Vision-based activity classification of excavators by bidirectional LSTM. Applied Sciences, 13(1), 272. https://doi.org/10.3390/app13010272 [CrossRef] [Google Scholar]
- Rao, A.S., Radanovic, M., Liu, Y., Hu, S., Fang, Y., Khoshelham, K., Palaniswami, M., & Ngo, T. (2022). Real-time monitoring of construction sites: Sensors, methods, and applications. Automation in Construction, 136, 104099. https://doi.org/10.1016/j.autcon.2021.104099 [CrossRef] [Google Scholar]
- Trivedi, S., & Patel, N. (2021). Virtual employee monitoring: A review on tools, opportunities, challenges, and decision factors. Empirical Quests for Management Essences, 1(1), 86-99. https://doi.org/10.2139/ssrn.3742085 [Google Scholar]
- Mujahid, A., Awan, M.J., Yasin, A., Mohammed, M.A., Damasevicius, R., Maskeliunas, R., & Abdulkareem, K.H. (2021). Real-time hand gesture recognition based on deep learning YOLOv3 model. Applied Sciences, 11(9), 4164. https://doi.org/10.3390/app11094164 [CrossRef] [Google Scholar]
- Nasseri, M.H., Moradi, H., Hosseini, R., & Babaee, M. (2021). Simple online and realtime tracking with occlusion handling. arXiv preprint. https://arxiv.org/abs/2103.04147 [Google Scholar]
- Rakkshab Iyer, Bhensdadiya, K.P., & Ringe, P.S. (2021). Comparison of YOLOv3, YOLOv5s and MobileNet-SSD v2 for real-time mask detection. ResearchGate. https://www.researchgate.net/publication/353211011 [Google Scholar]
- Jin, C., Zhu, Z., Bai, Y., Jiang, G., & He, A. (2020). A deep-learning-based scheme for detecting driver cell-phone use. IEEE Access, 8, 18580-18589. https://doi.org/10.1109/ACCESS.2020.2990320 [CrossRef] [Google Scholar]
- Chen, C., Zhu, Z., & Hammad, A. (2020). Automated excavators activity recognition and productivity analysis from construction site surveillance videos. Automation in Construction, 110, 103045. https://doi.org/10.1016ZJ.autcon.2019.103045 [CrossRef] [Google Scholar]
- Nath, N.D., & Behzadan, A.H. (2020). Deep convolutional networks for construction object detection under different visual conditions. Frontiers in Built Environment, 6, 97. https://doi.org/10.3389/fbuil.2020.00097 [CrossRef] [Google Scholar]
- Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint, https://arxiv.org/abs/1704.04861 [Google Scholar]
- Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., & Murphy, K. (2016). Speed/accuracy trade-offs for modern convolutional object detectors. arXiv preprint. https://arxiv.org/abs/1611.10012 [Google Scholar]
- Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016). Simple online and realtime tracking. In IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2016.7533003 [Google Scholar]
- Kim, H., Ahn, C.R., Engelhaupt, D., & Lee, S. (2018). Application of dynamic time warping to the recognition of mixed equipment activities in cycle time measurement. Automation in Construction, 87, 225-234. https://doi.org/10.1016/j.autcon.2017.12.014 [CrossRef] [Google Scholar]
- Memarzadeh, M., Golparvar-Fard, M., & Niebles, J.C. (2013). Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors. Automation in Construction, 32, 24-37. https://doi.org/10.1016/j.autcon.2012.12.002 [CrossRef] [Google Scholar]
- Azar, E.R., Dickinson, S., & McCabe, B. (2013). Server customer interaction tracker: Computer vision-based system to estimate dirt loading cycles. Journal of Construction Engineering and Management, 139(7), 785-794. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000652 [CrossRef] [Google Scholar]
- Husein, A.M., Calvin, N., Halim, D., Leo, R., & William, N. (2019). Motion detect application with frame difference method on a surveillance camera. Journal of Physics: Conference Series, 1230(1), 012017. https://doi.org/10.1088/1742-6596/1230/1/012017 [CrossRef] [Google Scholar]
- KiranKumar, C., & Rawal, K. (2022). A brief study on object detection and tracking. Journal of Physics: Conference Series, 2327(1), 012012. https://doi.org/10.1088/17426596/2327/1/012012 [CrossRef] [Google Scholar]
- Jacobsen, E.L., Teizer, J., & Wandahl, S. (2023). Work estimation of construction workers for productivity monitoring using kinematic data and deep learning. Automation in Construction, 152, 104932. https://doi.org/10.1016/j.autcon.2023.104932 [CrossRef] [Google Scholar]
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