Natural gas pipeline thermal insulation layer defect detection method based on machine vision

The invention discloses a natural gas pipeline thermal insulation layer defect detection method based on machine vision, which comprises the following steps: arranging a liquefied natural gas pipeline shooting environment, and shooting a surface picture of a to-be-detected liquefied natural gas pipeline thermal insulation layer; uploading the obtained picture to a computer end and counting the gray value of the obtained picture; performing noise reduction processing on the picture; training a gray threshold detection model by using a neural network; and identifying the thermal insulation layer defect of the to-be-detected pipeline, repairing the defect and re-inspecting. Compared with a traditional method, the method is higher in recognition efficiency, is easy to operate, and saves the labor cost. 本发明公开了一种基于机器视觉的天然气管线保温层缺陷检测方法，包括：布置液化天然气管线拍摄环境，拍摄待检测液化天然气管线保温层表面图片；将所得图片上传至电脑端并统计所得图片灰度值；对图片进行降噪处理；使用神经网络训练灰度阈值检测模型；识别待检测管线保温层缺陷，修复缺陷并重新送检。本发明相较于传统方法，识别效率更高，易于操作，节省人工成本。