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Article

Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions

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Department of Civil Engineering, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
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Department of Civil Engineering and Industrial Design, University of Liverpool, Liverpool L69 3BX, UK
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Department of Computer Science, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
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Author to whom correspondence should be addressed.
Academic Editors: Yangquan Chen, Subhas Mukhopadhyay, Nunzio Cennamo, M. Jamal Deen, Junseop Lee, Simone Morais and Fabio Tosti
Remote Sens. 2022, 14(1), 106; https://doi.org/10.3390/rs14010106
Received: 25 November 2021 / Revised: 12 December 2021 / Accepted: 24 December 2021 / Published: 27 December 2021
(This article belongs to the Topic Artificial Intelligence in Sensors)
Automatic damage detection using deep learning warrants an extensive data source that captures complex pavement conditions. This paper proposes a thermal-RGB fusion image-based pavement damage detection model, wherein the fused RGB-thermal image is formed through multi-source sensor information to achieve fast and accurate defect detection including complex pavement conditions. The proposed method uses pre-trained EfficientNet B4 as the backbone architecture and generates an argument dataset (containing non-uniform illumination, camera noise, and scales of thermal images too) to achieve high pavement damage detection accuracy. This paper tests separately the performance of different input data (RGB, thermal, MSX, and fused image) to test the influence of input data and network on the detection results. The results proved that the fused image’s damage detection accuracy can be as high as 98.34% and by using the dataset after augmentation, the detection model deems to be more stable to achieve 98.35% precision, 98.34% recall, and 98.34% F1-score. View Full-Text
Keywords: pavement defect detection; machine learning; thermal analysis; multichannel image fusion pavement defect detection; machine learning; thermal analysis; multichannel image fusion
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MDPI and ACS Style

Chen, C.; Chandra, S.; Han, Y.; Seo, H. Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions. Remote Sens. 2022, 14, 106. https://doi.org/10.3390/rs14010106

AMA Style

Chen C, Chandra S, Han Y, Seo H. Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions. Remote Sensing. 2022; 14(1):106. https://doi.org/10.3390/rs14010106

Chicago/Turabian Style

Chen, Cheng, Sindhu Chandra, Yufan Han, and Hyungjoon Seo. 2022. "Deep Learning-Based Thermal Image Analysis for Pavement Defect Detection and Classification Considering Complex Pavement Conditions" Remote Sensing 14, no. 1: 106. https://doi.org/10.3390/rs14010106

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