DSM-Net: A multi-scale detection network of sonar images for deep-sea mining vehicle

作者：Xinran Liu, Jianmin Yang, Wenhao Xu, Qihang Chen, Haining Lu, Yu Chai, Changyu Lu, Yulong Xue · 发表于：Applied Ocean Research · 年份：2025 · DOI：10.1016/j.apor.2025.104551 · 被引用次数：13 · 研究领域：Underwater Acoustics Research、Robotics and Sensor-Based Localization、Underwater Vehicles and Communication Systems

• This study introduces DSM-Net, a deep learning-based sonar image detection network designed for deep-sea mining vehicles, addressing challenges like multi-scale detection of seabed terrains and noise interference. • The network introduces two innovative modules, TSA and PDM, which enhance multi-scale feature extraction, reduce noise, and improve inference speed and detection accuracy. The proposed ADL function effectively addresses the issue of target imbalance. • Extensive experiments and sea trials validate the superior performance of DSM-Net in sonar image detection, ensuring operational and navigational safety for deep-sea mining vehicles, and supporting subsequent path planning and obstacle avoidance tasks. Deep-sea mining vehicles (DSMVs) play a crucial role in deep-sea mining operations, requiring high-precision, real-time detection of seabed rocks and terrain of varying scales to ensure safe navigation and operation. However, the complexity of multi-scale seabed terrains, along with the low resolution and high noise levels in sonar images, makes accurate real-time detection a challenge. To address these issues, DSM-Net, a multi-scale terrain detection network specifically designed for deep-sea mining, is proposed. DSM-Net integrates several innovative modules: the Tri-Scale Attention Module (TSA) extracts multi-scale features and reduces noise interference, the Partial-Dynamic Module (PDM) improves inference speed, and the ASFF* detection head incorporates an additi...