Application of GPR System With Convolutional Neural Network Algorithm Based on Attention Mechanism to Oil Pipeline Leakage Detection - Li, Jiadai; Yang, Ding; Guo, Cheng; Ji, Chenggao; Jin, Yangchao; Sun, Haijiao; Zhao, Qing (Frontiers in Earth Science, 2022)
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 paper. By taking the simulated ground penetrating radar (GPR) data as prior knowledge, the structure of convolutional neural network based on attention mechanism is constructed, and finally the location and working condition of underground oil pipeline are recognized in the simulation data and measured data. The simulation results show that after using new optimized convolutional neural network, the accuracy rates of the leakage discrimination from horizontal data acquired along oil pipeline and the targets classification from longitudinal data acquired perpendicular to oil pipeline are 94.5% and 84.6%, respectively. Compared with original convolutional neural network without attention mechanism, the accuracy rates of the leakage discrimination and the targets classification are improved by 6.2% and 7.8%, respectively. We further train measured data with optimized convolutional neural network, results show that compared with conventional network, 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 GPR oil pipeline recognition algorithm based on attention mechanism can well accomplish the identification of underground oil pipeline.