Distributed fiber optic warning identification algorithm for oil and gas pipelines based on the Inception-DVS model

Abstract

The paper propose a new lightweight Inception-DVS model for distributed fiber optic vibration warning of long-distance oil and gas pipelines. This model extends the advantages of the Inception network, designs a new model structure to adapt to complex environments, and effectively reduces the computational load and parameters of the model by optimizing the convolution kernel and hierarchical design. The experimental results show that the model maintains 91 % detection accuracy while the model size is only 3.519 MB and the detection speed reaches 3.2 s, which significantly improves the real-time and efficiency. Compared to traditional models, the Inception DVS model performs well in resource-constrained edge computing environments, accurately identifying multiple pipeline threat events such as manual mining, external intrusion, and vehicle travel, while significantly reducing maintenance costs. In addition, the paper also verified the application of the model in the actual pipeline, and the results show that our model can respond quickly and issue accurate early warnings, effectively reduce the workload of manual inspection, and improve the safety and reliability of pipeline operation.