Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network

Automatic underwater target detection plays a vital role in sonar image processing and analysis, and its core task is to discriminate target categories and achieve precise positioning. However, the sonar image is interfered by the seafloor reverberation noise and complex background, which brings more significant challenges to the accurate detection of sonar target. To achieve accurate detection of different categories targets in sonar image, we proposed an adaptive global feature enhancement network (AGFE-Net), which uses multi-scale convolution and attention mechanisms with global receptive field to obtain sonar image multi-scale semantic feature and enhance the correlation between features. Specifically, we use the multi-scale receptive field feature extraction block (MSFF-Block) and the self-attention mechanism block (SAM-Block) to enhance model feature extraction ability; the bidirectional feature pyramid network (Bi-FPN) and the global pyramid pooling block (GPP-Block) are used to obtain the deep semantic feature and suppress background noise interference; the adaptive feature fusion block (AFF-Block) is used to effectively fuse features of different scales. Experimental results on the presented sonar target detection dataset WH-Dataset and QD-Dataset validate the advantage of AGFE-Net over other state-of-the-art target detection methods.

Automated summary

The AGFE-Net proposed in this study utilizes multi-scale convolution and attention mechanisms with a global receptive field to enhance feature extraction, suppress background noise interference, and effectively fuse features of different scales in sonar image processing. Experimental results demonstrate the superiority of AGFE-Net in sonar target detection.