← 研踪 PaperTrailarXiv:2610.39371 · v1
Lightweight Underwater Acoustic Vessel Classification for Embedded Deployment
首次提交 2026-10-29 13:02
版本更新 2026-10-29 13:02
雷达观测 2026-10-30 11:25
摘要
We propose a Raspberry Pi-based underwater vessel classification system for edge deployment under limited connectivity, bandwidth and power in offshore acoustic surveillance. Using ShipsEar, we compare six classifiers on a Raspberry Pi 5 through a complete audio-to-prediction pipeline. We assess classification performance, processing latency, CPU utilisation, memory consumption and monitored power. Supporting experiments examine acoustic feature selection, statistical pooling and Transformer patch configurations. The two-layer convolutional neural network (CNN) achieved a mean macro-F1 of \textbf{(99.14 \textpm{} 0.32)\%} across five random seeds. The radial basis function support vector machine (RBF-SVM) achieved \textbf{98.83\%}, with a median end-to-end latency of 28.109~ms compared with 120.655~ms for the CNN. This favourable trade-off motivated RBF-SVM deployment on the Raspberry Pi Zero 2 W. It maintained the same classification performance with a median end-to-end latency of \textbf{349.09~ms} and a sustained throughput of \textbf{2.962 classifications/s}, or \textbf{14.8} times the rate required for consecutive five-second audio segments.
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v1 · 2026-10-29 13:02 · 最新观测版本
Lightweight Underwater Acoustic Vessel Classification for Embedded Deployment
We propose a Raspberry Pi-based underwater vessel classification system for edge deployment under limited connectivity, bandwidth and power in offshore acoustic surveillance. Using ShipsEar, we compare six classifiers on a Raspberry Pi 5 through a complete audio-to-prediction pipeline. We assess classification performance, processing latency, CPU utilisation, memory consumption and monitored power. Supporting experiments examine acoustic feature selection, statistical pooling and Transformer patch configurations. The two-layer convolutional neural network (CNN) achieved a mean macro-F1 of \textbf{(99.14 \textpm{} 0.32)\%} across five random seeds. The radial basis function support vector machine (RBF-SVM) achieved \textbf{98.83\%}, with a median end-to-end latency of 28.109~ms compared with 120.655~ms for the CNN. This favourable trade-off motivated RBF-SVM deployment on the Raspberry Pi Zero 2 W. It maintained the same classification performance with a median end-to-end latency of \textbf{349.09~ms} and a sustained throughput of \textbf{2.962 classifications/s}, or \textbf{14.8} times the rate required for consecutive five-second audio segments.
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