关键词: 合成孔径声呐, 浅表层目标探测, 线状目标, 语义分割, DeepLabv3+

Abstract: [Purposes] Addressing the lack of research on the detection of linear targets using synthetic aperture sonar (SAS) and the difficulty of obtaining precise contours with traditional bounding box detection，this paper aims to develop a precise detection method suitable for shallow-buried linear targets. [Methods] By analyzing the slender structural characteristics of linear targets，a pixel-level semantic segmentation strategy was employed to construct a detection model for shallow-buried linear targets based on the DeepLabv3+ semantic segmentation network.A dataset was constructed using real SAS images，and the model was trained using Kaiming initialization，the SGD optimizer，and a dynamic learning rate strategy. [Findings] Experimental results show that the model achieved a precision of 0.812，a recall of 0.756，a mean pixel accuracy of 0.824，and a mean intersection over union of 0.791，outperforming comparison models such as U-Net，which demonstrates the ability to accurately segment the edge contours of linear targets and effectively distinguish targets from the seabed background. [Conclusions] This method effectively enables the automated and precise detection of shallow-buried linear targets，providing a methodological foundation for the automated detection of such targets in large-area seabed topography images．

Key words: synthetic aperture sonar, shallow-buried target detection, linear targets, semantic segmentation, DeepLabv3+