Water pipe valve detection by using deep neural networks

Condition assessment of underground buried utilities, especially water distribution networks, is crucial to the decision making process for pipe replacement and rehabilitation. Hence, regular inspection of the water pipelines is carried out with in-pipe inspection robots to assess the internal condition of the water pipelines. However, the inspection robots need to identify and negotiate with the valves to pass through. Therefore, the aim of this study is to detect the valves in water pipelines in real-time to ensure smooth operation of the inspection robot. In this paper, four state-of-the-art deep neural network algorithms namely, Faster R-CNN, RFCN, SSD, and YOLO are presented to perform the real-time valve detection analysis. The study shows that Faster R-CNN, pre-trained with Resnet101 outperforms all the selected models by achieving 97:35% and 76:73% mean Average precison (mAP) values when the threshold for prediction is set to 50% and 75% respectively. However, in terms of the detection rate in frames per second (FPS), YOLOv3-608 seems to have better processing speed than all other models.