UAV image analysis for leakage detection in district heating systems using machine learning

In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional installation and end of service life.

Potentially, a leakage remains undiscovered for a very long period of time. Therefore, it is of great interest for power supply companies to monitor district heating networks to identify leakages. In this paper, the original IR images are captured in a 16 bit format by a UAV. On ground, potential leakages are extracted using a region extraction algorithm.

Thereafter a Convolutional Neural Network (CNN) as well as eight conventional Machine Learning (ML) classifiers are applied on these regions to classify whether or not it is a leakage. In total, twelve UAV sequences are captured at different cities in Denmark. Based on these, around 13.4 million samples of image patches of district heating systems are extracted.

Eleven sequences are used for training and the remaining one for testing. This was performed on all splits in the leave-one-out testing. The deep learning CNN achieved an average weighted accuracy of 0.872 with a false positive and negative rate of 12.7 % and 10.4 %, respectively. This CNN model detected around 98.6 % of the true leakages.

In comparison, conventional ML classifiers, i.e. Adaboost (AB), Random Forest (RF), etc. provide lower average weighted accuracy, but on the other hand they require less computational resources. We have compared our method with a state-of-art method and the result shows that the proposed method is very competitive.