WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra

The frequent occurrence of marine oil spills underscores the need for efficient methods to identify spilled substances and analyze their thickness. Traditional models based on Laser-Induced Fluorescence (LIF) technology often focus on a single functionality, limiting their ability to simultaneously perform qualitative and quantitative analyses. This study introduces a novel LIF-based spectral analysis method that integrates a self-designed detection system and a multi-task framework, the Wavelet CNN-sLSTM-KAN-Enhanced Transformer (WaveConv-sLSTM-KET). By combining a Wavelet Transform CNN block, a scalar LSTM block, and a Kolmogorov–Arnold Network-Enhanced Transformer block, the framework enables simultaneous oil-type identification and thickness prediction without preprocessing or fully connected layers. It achieves high classification accuracy and precise regression for oil film thicknesses (50 µm–0.5 mm). Its reliability, real-time operation, and lightweight structure address limitations of conventional methods, offering a promising solution for non-destructive, efficient oil spill detection.