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Fused Deep Neural Networks for Sustainable and Computational Management of Heat-transfer Pipeline Diagnosis by Honggeun Ji; ChaeHee An; Minyoung Lee; Jufeng Yang; Eunil Park is a Engineering article available to read on EtoBox.

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We propose deep learning-based models for the risk detection of underground pipelines. To build effective diagnosis models, we construct two types of deep neural network frameworks. First, we propose an image segmentation model with two parallel encoder structures for analyzing RGB and thermal images to detect risk-suspected regions due to pipeline rupture. In addition, for pipeline risk-stage recognition, we compare the performance of various image classification models and select the optimal model. Then, by integrating the image segmentation and classification models, we propose a fused model that performs risk-suspected region detection and risk-level detection using a single inference. The proposed image segmentation model achieves IoU and Dice coefficient values of 0.8373 and 0.9024, respectively, indicating higher performance in suspected-region detection compared with other competing models and demonstrating robust performance against false recognition. In risk-level detection, we confirm that the DenseNet-based model has the highest performance among the classification models, scoring 94.10% and 95.65% in terms of heat and leak accuracy, respectively. Finally, we validate t

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Author

Honggeun Ji; ChaeHee An; Minyoung Lee; Jufeng Yang; Eunil Park

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)