Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

Abstract

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 that the fusion model, which recorded 0.8160, 0.8711, 93.90%, and 95.05% in terms of IoU, Dice coefficient, heat accuracy, and leak accuracy, respectively, can process 30 frames of video per second in real-time at the cost of a slight drop in performance.