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

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 categorisatio...

Full description

Saved in:
Bibliographic Details
Published in:Buildings (Basel) 2022-02, Vol.12 (2), p.175
Main Authors: Heo, Seokjae, Han, Sehee, Shin, Yoonsoo, Lee, Myeunghun
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Classification
Construction industry wastes
Construction materials
construction waste
convolutional neural network
Data augmentation
deep learning
Fréchet inception distance
Household wastes
Learning
Manpower
Neural networks
Recycling
Transfer learning
YOLACT
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings12020175