Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging

Author:

Seo Hyungjoon1ORCID,Raut Aishwarya Deepak1,Chen Cheng2ORCID,Zhang Cheng2ORCID

Affiliation:

1. Department of Civil Engineering and Industrial Design, University of Liverpool, Liverpool L69 7WW, UK

2. Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, 111 Renai Road, Dushu Lake, Suzhou 215000, China

Abstract

In the era of the first Industrial Revolution, many buildings were built with red bricks, and the heritage buildings built at that time are more than 100 years old. In these old heritage buildings, damage is bound to occur due to chemical and physical effects. Technologies such as automatic damage detection can effectively manage damage, but they can be affected by other categories present in heritage buildings. Therefore, this paper proposes a CNN algorithm that can automatically detect cracks and damage that occur in heritage buildings, as well as multi-label classification, such as doors, windows, arches, artwork, brick walls, stonewalls, and vents. A total of 2400 thermal infrared images are collected for 8 categories and automatic classification was performed using the CNN algorithm. The average precision and average sensitivity for the eight categories of heritage buildings are 97.72% and 97.43%, respectively. This paper defines the causes of misclassification as the following two causes: misclassification by multiple objects and misclassification by the perception of the CNN algorithm.

Funder

Royal Society

Research England

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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