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http://dx.doi.org/10.21022/IJHRB.2020.9.4.335

Equipment and Worker Recognition of Construction Site with Vision Feature Detection  

Qi, Shaowen (Department of Civil Engineering, Tongji University)
Shan, Jiazeng (Department of Civil Engineering, Tongji University)
Xu, Lei (Shanghai Construction No.1 (Group) Co., Ltd.)
Publication Information
International Journal of High-Rise Buildings / v.9, no.4, 2020 , pp. 335-342 More about this Journal
Abstract
This article comes up with a new method which is based on the visual characteristic of the objects and machine learning technology to achieve semi-automated recognition of the personnel, machine & materials of the construction sites. Balancing the real-time performance and accuracy, using Faster RCNN (Faster Region-based Convolutional Neural Networks) with transfer learning method appears to be a rational choice. After fine-tuning an ImageNet pre-trained Faster RCNN and testing with it, the result shows that the precision ratio (mAP) has so far reached 67.62%, while the recall ratio (AR) has reached 56.23%. In other word, this recognizing method has achieved rational performance. Further inference with the video of the construction of Huoshenshan Hospital also indicates preliminary success.
Keywords
Object detection; Construction Site management; Transfer learning; CNN;
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