YOLOv5s Multimode Sensing-Based Pipe Network Detection and Inspection Method

In the field of pipe network detection, existing research has focused mainly on traditional manual pipe network detection methods, neglecting the need for accurate positioning and detection in highly dynamic and strong interference environments, as well as the need for pipe network detection and inspection methods under uncertain factors. This paper proposes a pipe detection method based on YOLOv5 multimode perception. A pipe detection model with YOLOv5 as the core and multimode perception data fusion as the basis is constructed using a mobile pipe network robot as the sample collection and detection operation platform. A neural network based on the YOLOv5 and YOLOv5S perceptron framework is designed, which uses traditional pipeline inspection technology and multimode data fusion technology. A convolutional neural network is used as the feature extraction module of the network. The robustness of the model is improved by optimizing the training of the YOLOv5 network on the basis of improved convolution and transfer learning techniques. The experimental results show that this method can accurately detect the pipe network structure and function, reduce the false detection rate, and improve the false alarm detection ability. In the identification stage of the pipe network, the robot can effectively deal with large amounts of noise and multicategory situations, improve the detection accuracy, and achieve high accuracy and reliability.