TY - JOUR
T1 - A semi-supervised learning detection method for vision-based monitoring of construction sites by integrating teacher-student networks and data augmentation
AU - Xiao, Bo
AU - Zhang, Yuxuan
AU - Chen, Yuan
AU - Yin, Xianfei
N1 - Funding Information:
The authors would like to thank Professor Shih-Chung Kang for publishing the ACID dataset to the community. More information about the ACID dataset can be found on the link: www.acidb.ca. We gratefully acknowledge the financial support of the National Natural Science Foundation of China (Grant No. 72002152).
Publisher Copyright:
© 2021 The Authors
PY - 2021/10
Y1 - 2021/10
N2 - Recently, deep-learning detection methods have achieved huge success in the vision-based monitoring of construction sites in terms of safety control and productivity analysis. However, deep-learning detection methods require large-scale datasets for training purposes, and such datasets are difficult to develop due to the limited accessibility of construction images and the need for labor-intensive annotations. To address this problem, this research proposes a semi-supervised learning detection method for construction site monitoring based on teacher–student networks and data augmentation. The proposed method requires a limited number of labeled data to achieve high detection performance in construction scenarios. Initially, the proposed method trains the teacher object detector with labeled data following weak data augmentation. Next, the trained teacher object detector generates pseudo-detection results from unlabeled images that have been weakly augmented. Finally, the student object detector is trained with the pseudo-detection results and unlabeled images that have been both weakly and strongly augmented. In our experiments, 10,000 annotated construction images from the Alberta Construction Image Dataset (ACID) have been divided into a training set (70%) and a validation set (30%). The proposed method achieved a 91% mean average precision (mAP) on the validation set while only requiring 30% of the training set. In comparison, the existing supervised learning method ResNet50 Faster R-CNN achieved a mAP of 90.8% when training on the full training set. These experimental results show the potential of the proposed method in terms of reducing the time, effort, and costs spent on developing construction datasets. As such, this research has explored the potential of semi-supervised learning methods and increased the practicality of vision-based monitoring systems in the construction industry.
AB - Recently, deep-learning detection methods have achieved huge success in the vision-based monitoring of construction sites in terms of safety control and productivity analysis. However, deep-learning detection methods require large-scale datasets for training purposes, and such datasets are difficult to develop due to the limited accessibility of construction images and the need for labor-intensive annotations. To address this problem, this research proposes a semi-supervised learning detection method for construction site monitoring based on teacher–student networks and data augmentation. The proposed method requires a limited number of labeled data to achieve high detection performance in construction scenarios. Initially, the proposed method trains the teacher object detector with labeled data following weak data augmentation. Next, the trained teacher object detector generates pseudo-detection results from unlabeled images that have been weakly augmented. Finally, the student object detector is trained with the pseudo-detection results and unlabeled images that have been both weakly and strongly augmented. In our experiments, 10,000 annotated construction images from the Alberta Construction Image Dataset (ACID) have been divided into a training set (70%) and a validation set (30%). The proposed method achieved a 91% mean average precision (mAP) on the validation set while only requiring 30% of the training set. In comparison, the existing supervised learning method ResNet50 Faster R-CNN achieved a mAP of 90.8% when training on the full training set. These experimental results show the potential of the proposed method in terms of reducing the time, effort, and costs spent on developing construction datasets. As such, this research has explored the potential of semi-supervised learning methods and increased the practicality of vision-based monitoring systems in the construction industry.
KW - Construction sites
KW - Data augmentation
KW - Deep learning
KW - Object detection
KW - Teacher-student networks
KW - Vision-based monitoring
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85112323148&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2021.101372
DO - 10.1016/j.aei.2021.101372
M3 - Article
AN - SCOPUS:85112323148
VL - 50
JO - Advanced engineering informatics
JF - Advanced engineering informatics
SN - 1474-0346
M1 - 101372
ER -