基于目标检测技术的油气管道安全智能监控设计与实现

Design and implementation of intelligent monitoring of oil and gas pipeline safety based on target detection technology

油气管道及周边安全监控是保障油气安全的重要手段,现有方法存在成本高、效率低、覆盖范围小等问题。为解决这些问题,基于目标检测技术,提出一种基于深度学习的油气管道及周边安全监控方法。该方法利用Flask框架搭建了一个Web检测系统,可对输入的图片、视频文件和视频流进行检测,并实现了多模型叠加检测、检测任务配置化等功能,使用YOLOv8作为目标检测模型,并在自行收集并标注的数据集上进行训练和测试。实验结果表明,该方法可以有效地检测出如明火、吸烟、施工车辆、未带安全帽、未穿工服、烟雾、未封口管口等违规场景,并与其他方案进行了对比,证明在费用、速度、精度、泛化能力等方面的优势。

The monitoring around oil and gas pipelines is an important means to ensure oil and gas safety.Existing methods have problems such as high cost,low efficiency,and small coverage.To solve these problems,based on ob⁃ject detection technology,a deep learning-based safety monitoring method for oil and gas pipelines and their sur⁃roundings was proposed.A web detection system was built by using the Flask framework,which could detect the in⁃put images,video files and video streams,and the functions of multi-model overlay detection and detection task configuration were realized.YOLOv8 was used as the target detection model,and trained and tested on the dataset collected and labeled by itself.Experimental results showed that this method could effectively detect irregular sce⁃narios such as open flames,smoking,construction vehicles,not wearing safety helmets,not wearing work clothes,smoke,unsealed pipe openings,etc.,and had compared with other schemes to prove the advantages of this method in terms of cost,speed,accuracy,and generalization ability。