油田作业现场近海周界区域入侵检测
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国家自然科学基金(61502094);黑龙江省高校基本科研业务费项目(KYCXTD201903)


Intrusion Detection of Offshore Area Perimeter in Oilfield Operation Site
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    摘要:

    为提高油田作业现场安全作业监管效率,保证油田作业现场近海周界区域人员闯入检测的准确性和时效性,针对现有的区域入侵检测方法在检测油田作业现场远距离小目标时效果差,达不到实时的问题,本文提出了一个基于SOLOv2和CenterNet结合的近海区域作业人员入侵检测模型,模型首先采用SOLOv2对作业现场近海区域周界进行分割,并对分割的结果进行识别,再将识别出的近海区域作为危险区域;然后使用CenterNet网络对作业人员进行检测与定位,对作业人员的位置与危险区域的位置进行计算,从而实现近海周界区域入侵检测.实验结果证明,该方法可有效解决作业现场背景复杂,检测准确率要求高,被检测目标小情况下的近海周界区域入侵检测问题,模型的准确率可达94.7%,该模型目前已经成功部署到油田作业现场,运行良好.

    Abstract:

    The existing regional intrusion detection methods for oilfield operation are exposed to problems of poor detection efficiency and failure in real-time detection due to the long distance and small targets. Therefore, this study proposes an offshore intrusion detection model to improve the safety supervision efficiency and ensure the accuracy and timeliness of intrusion detection in the offshore perimeter of oilfield operation sites. This model is based on the combination of SOLOv2 and CenterNet. First, the model uses SOLOv2 to segment the perimeter of the offshore area and identifies the danger area from these segmentation results. Then, CenterNet is adopted to detect and locate the operator and calculate the location of the operator and the danger area to realize the intrusion detection in the perimeter of the offshore area. The experimental results prove that this method can effectively solve the intrusion detection problems in offshore perimeter areas including complex backgrounds, high detection accuracy requirements, and small targets, and the accuracy of the model can reach 94.7%. This model has been successfully implemented in the oilfield operation sites with good performance.

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李婷玉,姜文文,邢金台,徐震,张蕾,田枫,刘芳.油田作业现场近海周界区域入侵检测.计算机系统应用,2022,31(1):236-241

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  • 收稿日期:2021-03-24
  • 最后修改日期:2021-04-21
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  • 在线发布日期: 2021-12-17
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