Shi, M; Chen, C; Xiao, B and Seo, J (2024) Vision-based detection method for construction site monitoring by integrating data augmentation and semisupervised learning. Journal of Construction Engineering and Management, 150(5): 04024027, ISSN 0733-9364
Abstract
Training deep learning models for vision-based monitoring of construction sites usually requires a large amount of labeled data. Semisupervised learning methods can efficiently obtain unlabeled data with substantial cost savings. Thus, this paper proposes a semisupervised object detection method for construction site monitoring. Weather as well as strong and weak data augmentation are integrated to cope with the complex construction site conditions (weather changes, camera view shifts, and so on) by integrating semisupervised learning to leverage the valid information in unlabeled construction site images. To validate its effectiveness, the proposed method was tested on the Alberta Construction Image Data Set (ACID), a public data set for the construction research community. The experimental results revealed that the proposed method achieves an average accuracy [mean average precision (mAP)] of 81.1% when trained on only 3% of the labeled images. This study helps to significantly reduce the development cost of vision-based object detection models for construction sites.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction management; object detection; semisupervised learning; weather data augmentation |
| Index terms: | deep learning, monitoring, effectiveness, object detection, accuracy, cost saving, construction site, weather |
| Subjects: | economics, computer vision, work location, air quality, control systems, artificial intelligence, professional development, performance management |
| Topics: | Sustainability, Information Management, Digital Applications, Quality Management, Cost Management, Site Management |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here