A submarine pipeline segmentation method for noisy forward-looking sonar images using global information and coarse segmentation

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The submarine pipeline is an important way to transport marine resources such as oil and gas. For maintaining submarine pipelines, autonomous underwater vehicles (AUVs) equipped with sonars are leveraged. Whether the complete submarine pipelines can be segmented from the complex and noisy background is the key to path planning and navigation, which determines the efficiency of AUVs. Existing methods for segmenting sonar images such as Markov random field (MRF) are insufficient to achieve this goal. Inspired by the similarities between submarine pipelines in sonar images and salient objects in natural images, we propose a novel method to extract pipelines which can be summarized as three steps. First, an effective denoising method via curvelet thresholding is proposed to suppress speckle noise. Second, we attempt to compute the probability whether a pixel belongs to submarine pipelines by global information containing four similarities. Two of them are shape similarity and angle similarity, which are proposed according to the characteristics of pipelines in sonar images. Third, we propose a coarse segmentation method based on bootstrapped support vector machines (SVMs) and a fusion strategy to generate binary segmentation results precisely and completely. We captured 15 sonar sequences via the AUV equipped with a forward-looking sonar at Qingdao National Deep-Sea Base and selected 262 sonar images from the sequences for evaluating the segmentation performance. The proposed method is compared with other advanced methods. The experimental results show that our method achieves a better performance than other methods, and demonstrate the feasibility of segmenting submarine pipelines inspired by saliency segmentation.