Abstract
With the increasing number of underwater pipeline investigation activities, the research on automatic pipeline detection is of great significance. At this stage, object detection algorithms based on Deep Learning (DL) are widely used due to their abilities to deal with various complex scenarios. However, DL algorithms require massive representative samples, which are difficult to obtain for pipeline detection with sub-bottom profiler (SBP) data. In this paper, a zero-shot pipeline detection method is proposed. First, an efficient sample synthesis method based on SBP imaging principles is proposed to generate samples. Then, the generated samples are used to train the YOLOv5s network and a pipeline detection strategy is developed to meet the real-time requirements. Finally, the trained model is tested with the measured data. In the experiment, the trained model achieved a mAP@0.5 of 0.962, and the mean deviation of the predicted pipeline position is 0.23 pixels with a standard deviation of 1.94 pixels in the horizontal direction and 0.34 pixels with a standard deviation of 2.69 pixels in the vertical direction. In addition, the object detection speed also met the real-time requirements. The above results show that the proposed method has the potential to completely replace the manual interpretation and has very high application value.
Highlights
IntroductionAs the offshore oil and gas industry grows, more and more pipelines are being laid into the ocean [1]
South China Sea, in which there are no pipe targets, are selected for sample synthesis, and the pipeline investigation data collected by Chirp III in Yangtze Estuary and Edgetech
The proposed sample synthesis method involves numerous variables, and some reasonable value ranges are listed in Table 1, depending on the actual circumstances that may be encountered during the measurement
Summary
As the offshore oil and gas industry grows, more and more pipelines are being laid into the ocean [1]. The pipeline routes and buried depth should be surveyed in time and stored as historical data [2,3]. The condition of the pipeline needs to be checked periodically in order to prevent damage to the pipeline from fishing activities, turbulence, tidal abrasion or sediment instability [4,5,6,7]. Sub-bottom profiler, as a kind of specially designed sonar to explore the first layers of sediment below the seafloor (usually over a thickness that commonly reaches tens of meters) [8] 372), can detect exposed and suspended pipelines and buried pipelines, and is widely used in underwater pipeline survey tasks Sub-bottom profiler, as a kind of specially designed sonar to explore the first layers of sediment below the seafloor (usually over a thickness that commonly reaches tens of meters) [8] (p. 372), can detect exposed and suspended pipelines and buried pipelines, and is widely used in underwater pipeline survey tasks
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