TY - GEN
T1 - Crack Detection of Masonry Structure Based on Infrared and Visible Image Fusion and Deep Learning
AU - Lu, Y. M.
AU - Huang, H.
AU - Zhang, C.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - From the standpoint of protecting and repairing the ancient city walls, this work aims to improve the feasibility and accuracy of crack detection in the brick wall background. Data sets of cracks in the surface of the ancient city walls were created, including RGB and thermal images. By using deep learning techniques, the best combination of data input type and network architecture were explored in the CNN-based training framework. The main contribution of this paper is: (a) a comprehensive dataset of cracks in the background of ancient city walls, including RGB images and infrared images; (b) a comparative analysis of crack detection results of different data fusion methods under different deep learning networks. Based on the results, the optimal data input and training network combination were identified for masonry wall crack identification, which enables an automatic crack damage detection for ancient city wall.
AB - From the standpoint of protecting and repairing the ancient city walls, this work aims to improve the feasibility and accuracy of crack detection in the brick wall background. Data sets of cracks in the surface of the ancient city walls were created, including RGB and thermal images. By using deep learning techniques, the best combination of data input type and network architecture were explored in the CNN-based training framework. The main contribution of this paper is: (a) a comprehensive dataset of cracks in the background of ancient city walls, including RGB images and infrared images; (b) a comparative analysis of crack detection results of different data fusion methods under different deep learning networks. Based on the results, the optimal data input and training network combination were identified for masonry wall crack identification, which enables an automatic crack damage detection for ancient city wall.
KW - Crack detection
KW - Deep learning
KW - Infrared and visible image fusion
KW - Masonry structure
UR - http://www.scopus.com/inward/record.url?scp=85189522685&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7965-3_25
DO - 10.1007/978-981-99-7965-3_25
M3 - Conference Proceeding
AN - SCOPUS:85189522685
SN - 9789819979646
T3 - Lecture Notes in Civil Engineering
SP - 275
EP - 284
BT - Towards a Carbon Neutral Future - The Proceedings of The 3rd International Conference on Sustainable Buildings and Structures
A2 - Papadikis, Konstantinos
A2 - Zhang, Cheng
A2 - Tang, Shu
A2 - Liu, Engui
A2 - Di Sarno, Luigi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Sustainable Buildings and Structures, ICSBS 2023
Y2 - 17 August 2023 through 20 August 2023
ER -