[1]田枫,白欣宇,刘芳,等.一种轻量化油田危险区域入侵检测算法[J].智能系统学报,2022,17(3):634-642.[doi:10.11992/tis.202107033]
TIAN Feng,BAI Xinyu,LIU Fang,et al.A lightweight intrusion detection algorithm for hazardous areas in oilfields[J].CAAI Transactions on Intelligent Systems,2022,17(3):634-642.[doi:10.11992/tis.202107033]
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TIAN Feng,BAI Xinyu,LIU Fang,et al.A lightweight intrusion detection algorithm for hazardous areas in oilfields[J].CAAI Transactions on Intelligent Systems,2022,17(3):634-642.[doi:10.11992/tis.202107033]
一种轻量化油田危险区域入侵检测算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
634-642
栏目:
人工智能院长论坛
出版日期:
2022-05-05
- Title:
- A lightweight intrusion detection algorithm for hazardous areas in oilfields
- Keywords:
- intrusion into hazardous areas in oilfields; target detection; depth separable convolution; lightweight; channel attention; deep learning; feature fusion; feature extraction
- 分类号:
- TP37
- 摘要:
- 油田危险区域入侵是油田安防领域的核心问题，以目标检测的方式捕获实时发生的危险是区域入侵任务的重点。为了提高模型的实时性，本文提出结合跨阶段线性瓶颈模块和通道注意力机制的轻量化YOLO检测算法。首先以轻量化卷积模块与跨阶段局部残差模块级联的跨阶段线性瓶颈模块搭建特征提取网络，大大减少了模型的参数量。在特征金字塔的特征融合模块前使用改进的通道注意力机制，增强特征的表达能力与特征的全局的关联性。在特征推理模块，使用中心归一化非极大值抑制方法进行输出优化，避免了对邻近目标的错误抑制。本算法在VOC2007数据集实验，精确率可达74.9%，优于大多轻量化检测算法，已在冀东油田部署应用，有效保证了油田作业人员的生命财产安全。
- Abstract:
- Hazardous areas in oilfields are a core problem in oilfield security, and capturing hazards occurring in real-time with target detection is the focus of intrusion detection models. To improve the real-time performance of such models, this paper proposes a lightweight YOLO detection algorithm combining the cross-stage linear bottleneck module and channel attention mechanism. First, a feature extraction network is built with the cross-stage linear bottleneck module cascaded with the lightweight convolutional and cross-stage local residual modules; this greatly reduces the parameters of the model. Then, an improved channel attention mechanism is used in front of the feature fusion module of the feature pyramid to enhance feature expressiveness and global relevance. In the feature inference module, the output is optimized using a central, normalized, nonmaximal suppression method to avoid false suppression of neighboring targets. This algorithm is tested in the VOC2007 dataset, and the accuracy rate can reach 74.9%, which is better than most lightweight detection algorithms. The algorithm has been deployed and applied in the Jidong Oilfield, where it effectively ensures the safety of life and property of oilfield operators.
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备注/Memo
收稿日期:2021-07-19。
基金项目:国家自然科学基金项目(61502094)；黑龙江省自然科学基金项目（LH2021F004）；黑龙江省优秀青年科学基金项目(YQ2020D001)；东北石油大学优秀中青年科研创新团队项目(KYCXTD201903).
作者简介:田枫，教授，博士生导师，东北石油大学计算机与信息技术学院院长，主要研究方向为计算机视觉、智能数据分析处理。主持和参与国家自然科学基金项目、国家科技重大专项项目8项。获授权专利16项，发表学术论文31篇;白欣宇，硕士研究生，主要研究方向为计算机视觉;刘芳，副教授，博士研究生，主要研究方向为智慧教育、多媒体与现代教育技术、计算机视觉、智能数据分析处理。获黑龙江省科技进步二等奖1项、大庆市科技进步二等奖1项，主持和参与国家自然科学基金项目、黑龙江省自然科学基金项目6项。发表学术论文22篇
通讯作者:刘芳.E-mail：lfliufang1983@126.com
更新日期/Last Update:
1900-01-01