Lightweight global adaptive feature enhancement network for underwater object detection with sonar image

Abstract Sonar target detection is widely used in various underwater detection tasks. However, sonar images often lack target information and blurry features because of the interference of seabed environmental noise and complex background information. This poses significant challenges for sonar target detection tasks. A new lightweight Global Adaptive Feature Enhancement Network (GAFE-Net) is proposed to enhance the ability to acquire target information and effectively suppress background information. This network utilizes lightweight convolutional calculations and adaptive feature extraction blocks with global feature extraction capabilities to capture multi-scale semantic features of sonar images. Specifically, GhostConv is used to maintain local feature extraction capability while reducing computation complexity. The adaptive feature extraction block (C2FC) is employed to capture deep semantic features. The partial self-attention (PSA) mechanism is adopted to enhance the capability of focusing on targets. The Slim-neck is deployed for fusing multi-scale information. Validation on the public sonar image dataset URPC2021 shows that, compared to other advanced sonar target detection algorithms, the proposed method improves accuracy while maintaining low computational complexity, demonstrating excellent performance.