Evaluating the Work Productivity of Assembling Reinforcement through the Objects Detected by Deep Learning
Open Access
Abstract
With the rapid development of deep learning, computer vision has assisted in solving a variety of problems in engineering construction. However, very few computer vision-based approaches have been proposed on work productivity’s evaluation. Therefore, taking a super high-rise project as a research case, using the detected object information obtained by a deep learning algorithm, a computer vision-based method for evaluating the productivity of assembling reinforcement is proposed. Firstly, a detector that can accurately distinguish various entities related to assembling reinforcement based on CenterNet is established. DLA34 is selected as the backbone. The mAP reaches 0.9682, and the speed of detecting a single image can be as low as 0.076 s. Secondly, the trained detector is used to detect the video frames, and images with detected boxes and documents with coordinates can be obtained. The position relationship between the detected work objects and detected workers is used to determine how many workers (N) have participated in the task. The time (T) to perform the process can be obtained from the change of coordinates of the work object. Finally, the productivity is evaluated according to N and T. The authors use four actual construction videos for validation, and the results show that the productivity evaluation is generally consistent with the actual conditions. The contribution of this research to construction management is twofold: On the one hand, without affecting the normal behavior of workers, a connection between construction individuals and work object is established, and the work productivity evaluation is realized. On the other hand, the proposed method has a positive effect on improving the efficiency of construction management.Keywords
Funding Information
- Science and Technology Research and Development Program of China Construction Eighth Engineering Division Co., Ltd (2019-3-17)
- Key Research and Development Program of Liaoning Province (2019010237-JH8/103)
This publication has 68 references indexed in Scilit:
- Smartphone-Based Mobile Testing Technique for Quick Bridge Cable–Force MeasurementJournal of Bridge Engineering, 2017
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Fully Convolutional Networks for Semantic SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment2016
- Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional NetworksIEEE Transactions on Medical Imaging, 2016
- Hardhat-Wearing Detection for Enhancing On-Site Safety of Construction WorkersJournal of Construction Engineering and Management, 2015
- Vision-Based Tower Crane Tracking for Understanding Construction ActivityJournal of Computing in Civil Engineering, 2014
- A vision-based motion capture and recognition framework for behavior-based safety managementAutomation in Construction, 2013
- A novel approach for the structural identification and monitoring of a full-scale 17-story building based on ambient vibration measurementsSmart Materials and Structures, 2008
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998