Application of GPR System With Convolutional Neural Network Algorithm Based on Attention Mechanism to Oil Pipeline Leakage Detection

Abstract

High-efficiency and high-quality detection of oil pipeline will significantly reduce environmental pollution and economic loss, so an unconventional oil pipeline anomaly detection convolutional neural network (CNN) algorithm based on attention mechanism is proposed in this article. By taking the simulated ground-penetrating radar (GPR) data as prior knowledge, the structure of the convolutional neural network based on the attention mechanism is constructed, and finally, the location and working condition of the underground oil pipeline are recognized in the simulation data and measured data. The simulation results show that after using the new optimized convolutional neural network, the accuracy rates of the leakage discrimination from horizontal data acquired along the oil pipeline and the classification of the target from longitudinal data acquired perpendicular to the oil pipeline are 94.5% and 84.6%, respectively. Compared with the original convolutional neural network without an attention mechanism, the accuracy rates of the leakage discrimination and the classification of the target are improved by 6.2% and 7.8%, respectively. We further train measured data with an optimized convolutional neural network, results show that compared with a conventional network, the new network can increase the corresponding accuracy rates of the leakage discrimination and the targets classification by 5.4% and 6.9%, reaching 92.3% and 84.4%, respectively. According to our study, the ground-penetrating radar oil pipeline recognition algorithm based on an attention mechanism can well accomplish the identification of underground oil pipelines.