Measurement of Construction Material Quantity through Analyzing Images Acquired by Drone And Data Augmentation


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 1, pp. 33-38, Jan. 2020
https://doi.org/10.3745/KTSDE.2020.9.1.33, Full Text:
Keywords: Drone, UAV, RCNN, Deep Learning, Counting Number, Construction Material
Abstract

This paper proposes a technique for counting construction materials by analyzing an image acquired by a Drone. The proposed technique use drone log which includes drone and camera information, RCNN for predicting construction material type, dummy area and Photogrammetry for counting the number of construction material. The existing research has large error ranges for predicting construction material detection and material dummy area, because of a lack of training data. To reduce the error ranges and improve prediction stability, this paper increases the training data with a method of data augmentation, but only uses rotated training data for data augmentation to prevent overfitting of the training model. For the quantity calculation, we use a drone log containing drones and camera information such as Yaw and FOV, RCNN model to find the pile of building materials in the image and to predict the type. And we synthesize all the information and apply it to the formula suggested in the paper to calculate the actual quantity of material pile. The superiority of the proposed method is demonstrated through experiments.


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Cite this article
[IEEE Style]
J. Moon, N. Song, J. Choi, J. Park and G. Kim, "Measurement of Construction Material Quantity through Analyzing Images Acquired by Drone And Data Augmentation," KIPS Transactions on Software and Data Engineering, vol. 9, no. 1, pp. 33-38, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.1.33.

[ACM Style]
Ji-Hwan Moon, Nu-Lee Song, Jae-Gab Choi, Jin-Ho Park, and Gye-Young Kim. 2020. Measurement of Construction Material Quantity through Analyzing Images Acquired by Drone And Data Augmentation. KIPS Transactions on Software and Data Engineering, 9, 1, (2020), 33-38. DOI: https://doi.org/10.3745/KTSDE.2020.9.1.33.