Abstract:

A method for identifying and locating pipeline insulation damage in refining and chemical enterprises using computer vision is proposed to address the issue of heat dissipation loss in pipeline insulation layers. Images are collected using infrared thermal imaging technology mounted on drones, and features are extracted using the ResNet50 architecture, combined with the FasterRCNN neural network to accurately identify and locate damage. Key steps such as image preprocessing, dataset construction, model training, and predictive maintenance are described. The deep integration of drone and computer vision technology significantly improves detection efficiency and accuracy. Moreover, the generated insulation damage distribution and positioning map allows enterprises to formulate data-driven maintenance strategies, reducing operational and maintenance costs and extending the service life of pipeline insulation, thereby improving overall operational efficiency. This provides scientific support for the long-term safe and stable operation of the pipeline system.

Key words: Pipeline, Insulation Damage, Infrared Thermal Imaging, Image Features, Target Recognition, Neural Network, Positioning Detection, Predictive Maintenance

摘要：

针对炼化企业中管道保温层散热损失问题，提出一种利用计算机视觉识别炼化企业管道保温破损并定位的方法。通过无人机搭载红外热成像技术进行图像采集，利用ResNet50架构进行特征提取，并结合FasterRCNN神经网络实现破损的准确识别与定位，阐述了图像预处理、数据集构建、模型训练及预知性维修等关键步骤，通过深度融合无人机与计算机视觉技术，显著提升了检测效率和精度。此外，利用生成的保温破损分布定位图，企业可制定数据驱动的维护策略，降低运维成本并延长管道保温使用寿命，从而提高整体运营效率，为管道系统的长期安全稳定运行提供了科学支持。

关键词: 管道, 保温破损, 红外热成像, 图像特征, 目标识别, 神经网络, 定位检测, 预知性维修