A Lightweight Underwater Target Detection Network for Forward-Looking Sonar Images

With the increasing demand for automatic underwater object detection, different deep learning (DL) methods have been performed on forward-looking sonar (FLS) images in recent years. However, DL-based approaches face challenges of overfitting due to excessive parameters and limited generalization ability. This study aims to develop a lightweight FLS detection network (FLSD-Net) that improves the detection of small underwater targets by addressing overfitting and generalization issues. The FLSD-Net incorporates three key modules—a light initial downsampling (LID) module to reduce information loss, a lightweight feature extraction (LFE) module to reduce overfitting from parameter redundancy, and an optimized network structure prioritizing high-resolution shallow layers to improve the detection accuracy and speed. Validation on public and self-created datasets shows that the proposed method achieved a balance of detection speed and accuracy with a better performance than state-of-the-art (SOTA) lightweight networks. In addition, the results in generalization experiments exhibit reduced missed and false detections, indicating improved robustness. This article advances underwater target detection by developing a lightweight network with improved generalization, providing a robust solution for real-world applications.