海洋测绘2024，Vol.44Issue(4)：16-20,5.DOI:10.3969/j.issn.1671-3044.2024.04.004
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
摘要
Abstract
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.关键词
浅地层剖面仪/水下管线调查/Frangi滤波/目标检测/深度学习/状态诊断Key words
sub-bottom profiler/underwater pipeline survey/Frangi filtering/object detection/deep learning/status diagnosis分类
天文与地球科学引用本文复制引用
郑根,赵建虎,苑明哲,杨文林..基于浅剖图像的海底管线状态自动诊断方法[J].海洋测绘,2024,44(4):16-20,5.基金项目
广东省自然资源厅海洋六大产业专项项目(GDNRC[2023]32). （GDNRC[2023]32）
