Lightweight Convolutional Neural Network Based Algorithm for Target Electric Field Line Spectrum Detection in Small Sample Size Scenarios

LIU Qi; ZHENG Wei

doi:10.7643/ issn.1672-9242.2025.09.008

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Equipment Environmental Engineering ›› 2025, Vol. 22 ›› Issue (9) : 68-77. DOI: 10.7643/ issn.1672-9242.2025.09.008

Special Topic—Reliability of Ship Equipment

Lightweight Convolutional Neural Network Based Algorithm for Target Electric Field Line Spectrum Detection in Small Sample Size Scenarios

LIU Qi¹, ZHENG Wei²

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Abstract

The work aims to achieve effective detection of target axial frequency electric field line spectra under complex ocean noise interference and small sample conditions. A physically guided data augmentation mechanism was employed to generate simulation data by frequency-shift scaling, energy modulation, and noise perturbation strategies. A lightweight multi-scale CNN architecture was constructed, incorporating depth-wise separable convolution and frequency-shift attention modules. Furthermore, a cross-domain transfer optimization strategy was utilized to facilitate knowledge transfer. The algorithm's performance was verified on a hybrid training set comprising 10 groups of real-world data and 200 groups of simulated data. At a signal-to-noise ratio (SNR) of -5 dB, the complete detection of the line spectra of target signal's main frequencies within a 10 s duration could be achieved. Compared with five traditional methods, the proposed algorithm demonstrated a line spectrum detection mean squared error (MSE) of just 0.001 5, an average absolute error of only 0.032 Hz, and a 0.1 Hz accuracy of 96.1%, showing superior detection accuracy and robustness, as well as a better overall performance. This paper presents a deep integration of physical modeling and deep learning, offering an effective solution for detecting small-sample axial frequency electric field signals in the presence of complex ocean noise. It provides a highly robust and low-data-dependent approach for underwater target detection, while also laying a theoretical foundation for future exploration of multi-physical field joint perception and online incremental learning mechanisms.

Key words

axial-frequency electric field / small sample size / line spectrum detection / lightweight CNN / data augmentation / cross-domain transfer learning

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LIU Qi, ZHENG Wei. Lightweight Convolutional Neural Network Based Algorithm for Target Electric Field Line Spectrum Detection in Small Sample Size Scenarios[J]. Equipment Environmental Engineering. 2025, 22(9): 68-77 https://doi.org/10.7643/ issn.1672-9242.2025.09.008

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