吉林大学学报（信息科学版）2024，Vol.42Issue(6)：1090-1099,10.
基于3D注意力残差的井场偷油行为识别算法
Algorithm for Identifying Oil Stealing Behavior in Wellsite Based on 3D Attention Residual
摘要
Abstract
The phenomenon of oil theft at well sites is an important issue that affects the safe production and stable operation of oil fields.The current behavior recognition methods pay less attention to the need for detecting oil theft in well pads,and there are often limitations in the application of the oil theft target feature recognition process.An algorithm for identifying oil theft behavior at well sites is proposed based on 3D attention residuals.This network consists of multiple three-dimensional attention residual blocks,which embed channels and spatiotemporal attention modules in each residual block to obtain more feature discrimination information and enhance the model's attention to important features.The effectiveness of the algorithm is varified on the dataset of oil theft behavior at the well site.The experimental results indicate that,compared to similar algorithms,this method has higher recognition accuracy.It can provide a reference for the practical application of automatic detection of oil theft behavior in oilfield well sites.关键词
井场偷油/三维卷积/行为识别/残差模块/注意力机制Key words
oilfield theft/3D convolution/behavior recognition/residual module/attention mechanism分类
信息技术与安全科学引用本文复制引用
张岩,肖坤,汪靖哲,张林军..基于3D注意力残差的井场偷油行为识别算法[J].吉林大学学报（信息科学版）,2024,42(6):1090-1099,10.基金项目
东北石油大学特色科研团队"智慧油田信息处理创新团队"基金资助项目(2023TSTD-04) （2023TSTD-04）
