TinyLSN: A Lightweight Network for Real-Time Marine Pipeline Leakage Detection in IoT Systems
Yuchen Lu, Yuxuan Zhang, Hongbing Liu, Sebastian Bader
Abstract
Intelligent acoustic emission-based pipeline leak detection technology plays a critical role in Internet of Things structural health monitoring for offshore platforms. However, traditional deep networks possess large parameter counts and high computational complexity, making them infeasible for deployment on resource-constrained edge nodes, while lightweight methods universally adopt single-scale feature extraction and cannot simultaneously capture short-duration burst and long-range attenuation characteristics of acoustic emission signals, resulting in insufficient discriminative capability for adjacent valves. To address this, this paper proposes Tiny Leak Sense Net (TinyLSN), a novel lightweight leak localization framework specifically designed for Internet of Things edge nodes. TinyLSN achieves optimal balance between computational efficiency and detection performance through three innovative components we designed including the Inverted Residual Block (IRB), Multi-Scale Dilated Perception Module (MSDPM), and Large Kernel Feed-Forward Network (LK-FFN), which respectively enhance cross-channel interactions, capture multi-scale temporal features, and extract global attenuation patterns. On our self-constructed experimental dataset simulating real offshore platform operational pipeline leakage, TinyLSN achieved detection accuracy of 97.11% to 97.45% and an extremely low false positive rate of 0.27% to 0.32%, significantly outperforming lightweight baseline methods. Validation on publicly available benchmark datasets further confirmed its generalization capability. When deployed on the STM32H7B3I-DK microcontroller, TinyLSN requires only 267.16 KiB Flash memory and achieves 3.417 ms inference latency. Furthermore, TinyLSN maintains over 90% accuracy under strong noise and achieves 94.83% accuracy with only 10% training samples, fully validating its reliability in harsh industrial environments and providing an efficient and feasible solution for offshore platform Internet of Things edge intelligence.