Zero-sample automatic detection of SBP pipeline images using deep learning
Aiming at the problems of low efficiency and low accuracy in the detection of existing submarine pipeline by manually interpreting the Sub-bottom profiler(SBP)image,this paper proposes an automatic detection method of submarine pipeline in SBP image based on zero sample deep learning,which takes into account the imaging mechanism.Firstly,the working principle of SBP and the characteristics of pipeline imaging are studied;Then,taking into account the imaging background and various practical factors,the pipeline sample graph is generated based on the imaging mechanism;After that,the generated pipeline samples are used to train YOLOv5 neural network,and the pipeline detection model in SBP graph is constructed.The automatic detection of pipeline in SBP graph is realized,and the correct detection rate is better than 90%.The proposed method provides a new way for the detection of submarine pipeline in SBP graph.
万方数据
维普
