面向边端部署应用的石油管道渗漏状态检测方法  

Oil Pipeline Leak Detection Method for Edge Deployment Applications

作　　者：许业鹏 易涤非 彭昱维 郝天皓 白静蕾 XU Yepeng;YI Difei;PENG Yuwei;HAO Tianhao;BAI Jinglei(CNOOC(Chongqing)Safety Equipment Co.,Ltd.,Chongqing 408399,China;Beijing University of Technology,Beijing 100124,China)

机构地区：[1]中海油(重庆)安全装备有限公司,重庆408399 [2]北京工业大学信息科学技术学院,北京100124

出　　处：《计量科学与技术》2025年第2期55-60,72,共7页Metrology Science and Technology

摘　　要：石油生产运输安全是保障能源稳定供应的重要条件。利用机器人对石油生产设备的状态检测能有效预防事故和降低风险。为此,研究了一种石油管道渗漏状态检测方法。该方法首先设计了一个基于残差金字塔特征增强模块,利用其多尺度分析能力以捕捉图像的细节信息,提升对渗漏状态特征和复杂背景的判别性;然后提出一种基于信息熵度量的模型压缩策略,提升边端设备部署条件下的石油管道渗漏状态检测效率。在某石油场站真实数据集上进行了实验以证明该方法的有效性,与基础模型相比,模型参数压缩率提升至56.43%,实现了44.29%的浮点运算数降低,减少了28.60%的参数量,而检测精度仅下降了0.02%。Oil production and transportation safety is essential for ensuring a stable energy supply.Using robots to monitor the condition of oil production equipment can effectively prevent accidents and reduce risks.This paper presents a method for detecting leakage states in oil pipelines.First,a feature enhancement module based on residual pyramid structure is designed to capture image details and improve the discrimination between leakage state features and complex backgrounds through its multi-scale analysis capability.Then,a model compression strategy based on information entropy metrics is proposed to improve detection efficiency under edge device deployment conditions.Experiments conducted on a real dataset from an oil field demonstrate the effectiveness of the proposed method.Compared to the baseline model,the model parameter compression rate increased to 56.43%,achieving a 44.29%reduction in floating-point operations and a 28.60%reduction in parameter count,while detection accuracy decreased by only 0.02%.

关 键 词：计量学 特征增强 模型压缩 信息熵 状态检测 边端部署 

分 类 号：TB9[一般工业技术—计量学]