基于浅剖图像的海底管线状态自动诊断方法

A real-time detection method for underwater pipeline in side scan sonar images based on semantic segmentation

为填补SBP(sub-bottom profiler,SBP)图像水下管线掩埋状态自动诊断研究空白以及提升水下管线巡检的自动化程度,给出了一套完整的水下管道掩埋状态自动诊断方法与流程。首先利用高效数据预处理方法准确还原了管线真实信息;其次基于Frangi滤波增强技术实现了海底线的准确提取;然后利用深度学习技术实现了管线目标的高可靠性检测;最后,给出了管线掩埋状态的判断准则,利用管线检测结果与海底之间的位置关系自动判断出管线的掩埋状态。利用多种型号浅地层剖面仪实测数据进行实验,结果表明,水下管线的检测精度可以达到了0.952的召回率和0.962的平均精度均值,基于目标检测结果能够实现管线掩埋状态的准确诊断。

To fill the research gap in automatic diagnosis of underwater pipeline burial status using SBP images and improve the automation level of underwater pipeline inspection,a complete set of automatic diagnosis methods and processes for underwater pipeline burial status has been provided.Firstly,efficient data preprocessing methods were used to accurately restore the true information of pipelines.Secondly,accurate extraction of seabed lines was achieved based on Frangi filter enhancement technology.Then,deep learning technology was used to achieve high reliability detection of pipeline targets.Finally,criteria for determining the burial status of pipelines was provided,and the burial status of pipelines was automatically determined using the positional relationship between pipeline detection results and the seabed.Experiments were conducted using measured data from various types of shallow layer profilers,and the results showed that the detection accuracy of underwater pipelines can reach a Recall of 0.952 and a mAP of 0.962.Based on the target detection,accurate diagnosis of pipeline burial status can be achieved.