轻量级模型在嵌入式设备上实现高效舰船辐射噪声分类
Ultra-Lightweight Network for Ship-Radiated Sound Classification on Embedded Deployment
- 采用频敏卷积与分组卷积结合，兼顾性能与计算效率
- 仅用3.9万参数实现71.45%宏平均F1分数，推理延迟仅6.05毫秒
- 适合资源受限的实时海上监测系统部署
本文提出ShuffleFAC，一种面向资源受限海事监测系统的轻量级声学模型，用于舰船辐射噪声分类。该模型在高效主干网络中引入频敏卷积，结合可分离卷积、点卷积分组和通道混洗技术，实现低计算开销下的频域特征提取。在DeepShip数据集上的实验表明，ShuffleFAC（γ=16）以39K参数和3.06M MACs达到71.45±1.18%的宏平均F1分数，在Raspberry Pi上的推理延迟为6.05±0.95ms。相比MicroNet0，其宏平均F1提升1.82%，模型规模缩小9.7倍，延迟降低2.5倍。结果证明ShuffleFAC适用于实时嵌入式水下声学目标识别。
原文摘要 · Abstract (English)
This letter presents ShuffleFAC, a lightweight acoustic model for ship-radiated sound classification in resource-constrained maritime monitoring systems. ShuffleFAC integrates Frequency-Aware convolution into an efficiency-oriented backbone using separable convolution, point-wise group convolution, and channel shuffle, enabling frequency-sensitive feature extraction with low computational cost. Experiments on the DeepShip dataset show that ShuffleFAC achieves competitive performance with substantially reduced complexity. In particular, ShuffleFAC ($γ=16$) attains a macro F1-score of 71.45 $\pm$ 1.18% using 39K parameters and 3.06M MACs, and achieves an inference latency of 6.05 $\pm$ 0.95ms on a Raspberry Pi. Compared with MicroNet0, it improves macro F1-score by 1.82 % while reducing model size by 9.7x and latency by 2.5x. These results indicate that ShuffleFAC is suitable for real-time embedded UATR.
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