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

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