2025 article
End-to-end underwater object recognition using multipolarization image fusion with single-photon LiDAR
(L. L. Grewe, E. P. Blasch, & I. Kadar, Eds.).
Underwater imaging faces significant challenges due to light scattering, absorption, and low contrast, which hinder object detection. Traditional single-polarization systems are often unable to reveal crucial object features in turbid waters. This study introduces a novel framework combining multi-polarization imaging with single-photon detection to enhance underwater object detection. By capturing 16 polarization-resolved images per scan, we leverage the diversity across polarization states to reveal features typically obscured in conventional systems. Using advanced image fusion and deep learning models, our approach improves detection accuracy, contrast, and signal-to-noise ratio. Preliminary results demonstrate enhanced detection performance, revealing critical features that are otherwise imperceptible. This method holds promise for applications in marine exploration, underwater robotics, and environmental monitoring.