A Lightweight Underwater Target Detection Network for Forward-Looking Sonar Images
Huiling Yang, Tian Zhou, Haolai Jiang, Xiaoyang Yu, Sen Xu
Abstract
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.