Training Data Sensitivity Analysis of Deep Neural Network for Differentiating Construction Laborers with/without Safety Helmets-Reference-Cited by-同舟云学术

Training Data Sensitivity Analysis of Deep Neural Network for Differentiating Construction Laborers with/without Safety Helmets

Published:2024-01-25 Issue: Volume:181 Page:516-524
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Kwon Young Ho¹²¹³⁴,Park Sangyoon¹²¹³⁴,Minh Luan Ta¹²¹³⁴,Oh Sehoon¹²¹³⁴,Heo Joon¹²¹³⁴

Affiliation:

1. Master’s Student, SCSI Lab, Dept. of Civil and Environmental Engineering, Yonsei Univ.

2. Ph.D. Candidate, SCSI Lab, Dept. of Civil and Environmental Engineering, Yonsei Univ. Email:

3. Undergraduate Student, Dept. of Civil and Environmental Engineering, Yonsei Univ. Email:

4. Professor, SCSI Lab, Dept. of Civil and Environmental Engineering, Yonsei Univ..

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485248.062

Reference30 articles.

1. Berrar, D. (2019). Cross-Validation.

2. Chen, H., Xiong, F., Wu, D., Zheng, L., Peng, A., Hong, X., Tang, B., Lu, H., Shi, H., and Zheng, H. (2017, November). Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1160–1165). IEEE.

3. Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018). In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3339–3348).

4. Semantic segmentation for partially occluded apple trees based on deep learning;Chen Z.;Computers and Electronics in Agriculture,2021

5. Farahani A. Voghoei S. Rasheed K. and Arabnia H. R. (2021). A brief review of domain adaptation. Advances in Data Science and Information Engineering: Proceedings from ICDATA 2020 and IKE 2020 877–894.