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Volume 12
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10.3390/buildings12020175
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Article

Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning

by
Seunguk Na
1,
Seokjae Heo
1,*,
Sehee Han
1,
Yoonsoo Shin
1 and
Myeunghun Lee
2
1
Department of Architectural Engineering, College of Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Gyeonggi-do, Korea
2
SpaceMind Co., Ltd., 219-2, 55 Hanyangdaehak-ro, Sangrok-gu, Ansan-si 15588, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Buildings 2022, 12(2), 175; https://doi.org/10.3390/buildings12020175
Submission received: 22 December 2021 / Revised: 24 January 2022 / Accepted: 31 January 2022 / Published: 4 February 2022
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Abstract

The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model.
Keywords: deep learning; convolutional neural network; recycling; YOLACT; Fréchet inception distance; construction waste; data augmentation; transfer learning deep learning; convolutional neural network; recycling; YOLACT; Fréchet inception distance; construction waste; data augmentation; transfer learning

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MDPI and ACS Style

Na, S.; Heo, S.; Han, S.; Shin, Y.; Lee, M. Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning. Buildings 2022, 12, 175. https://doi.org/10.3390/buildings12020175

AMA Style

Na S, Heo S, Han S, Shin Y, Lee M. Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning. Buildings. 2022; 12(2):175. https://doi.org/10.3390/buildings12020175

Chicago/Turabian Style

Na, Seunguk, Seokjae Heo, Sehee Han, Yoonsoo Shin, and Myeunghun Lee. 2022. "Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning" Buildings 12, no. 2: 175. https://doi.org/10.3390/buildings12020175

APA Style

Na, S., Heo, S., Han, S., Shin, Y., & Lee, M. (2022). Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning. Buildings, 12(2), 175. https://doi.org/10.3390/buildings12020175

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