International Journal of High-Rise Buildings (국제초고층학회논문집)

Council on Tall Building and Urban Habitat Korea (한국초고층도시건축학회)
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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.)
  • Published : 2020.12.01
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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.

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References

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