Formwork detection in UAV pictures of construction sites

The monitoring of the construction progress is an essential task on construction sites, which nowadays is conducted mostly by hand. Recent image processing techniques provide a promising approach for reducing manual labor on site. While modern machine learning algorithms such as convolutional neural networks have proven to be of sublime value in other application fields, they are widely neglected by the CAE industry so far. In this paper, we propose a strategy to set up a machine learning routine to detect construction elements on UAV photographs of construction sites. In an accompanying case study using 750 photographs containing nearly 10.000 formwork elements, we reached accuracies of 90% when classifying single object images and 40% when locating formwork on multi-object images. telligence approach to recognize and locate construction elements on site. In the first part of the paper, we give an overview of the state of the art in image analysis as used on construction sites today, followed by a further description of the used methodology. We conclude the paper with a proof of concept and a summary of our results.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[3]  D 4 AR – A 4-DIMENSIONAL AUGMENTED REALITY MODEL FOR AUTOMATING CONSTRUCTION PROGRESS MONITORING DATA COLLECTION , PROCESSING AND COMMUNICATION , 2022 .

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[6]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[7]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[8]  Markus König,et al.  Interior construction state recognition with 4D BIM registered image sequences , 2018 .

[9]  Mani Golparvar-Fard,et al.  Potential of big visual data and building information modeling for construction performance analytics: An exploratory study , 2017 .

[10]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ausif Mahmood,et al.  A Framework for Designing the Architectures of Deep Convolutional Neural Networks , 2017, Entropy.