Heating pipeline identification and leakage detection method based on improved R3Det

Abstract

In response to the frequent occurrence of leakage accidents in heating pipelines, timely detection of leakage points in such pipelines is of great significance to ensure the safe operation of heating systems. This article proposes a method for detecting leakage points in heating pipelines using drones equipped with infrared thermal imagers, employing a combination of the improved R3Det algorithm and the adaptive threshold method. Firstly, the algorithm identifies the area of the heating pipeline and then employs the adaptive threshold method to detect the presence of leakage points in the identified pipeline area. Additionally, taking into account the morphological characteristics of heating pipelines, the R3Det network is enhanced by introducing variable convolution, enabling more precise extraction of pipeline features. To reduce model overfitting and enhance network expression capabilities, the H-swish activation function is employed to replace the original activation function. Furthermore, candidate anchor boxes are clustered using the K-means++ clustering algorithm to obtain better position regression results and improve training efficiency. The improved algorithm demonstrates significantly better positioning precision compared to the original network. Moreover, an adaptive threshold algorithm is proposed for leak detection and labelling, utilising the original temperature information contained in infrared images. The experimental results demonstrate that this method achieves higher accuracy in detecting leaks in heating pipelines.