Dynamic vision-based underwater optical signal detection system in a degraded environment using multi-dimensional integral imaging and deep learning

Underwater optical signal detection in severely degraded environments remains a challenging task. In this work, we propose what we believe to be a novel dynamic vision-based underwater optical signal detection system. The signal detection performance is evaluated in a degraded underwater environment, including partial occlusion and turbidity. The system utilizes dynamic vision sensors from an event camera array, multi-dimensional integral imaging, and deep learning networks to achieve signal detections. In the experiment, optical signals are transmitted using a modulated light-emitting diode. The optical signals, after propagating through the degraded underwater environment, are captured by the event camera array in the form of event sequences. The event sequences are preprocessed as multi-dimensional event videos. The videos are classified by the vision transformer and gated recurrent unit network (ViT-GRU). The proposed system is compared to other relevant state-of-the-art frame-based approaches in terms of the detection performance evaluated by the Matthew correlation coefficient and the number of error symbols. For the experiments we conducted, the proposed dynamic vision-based underwater optical signal detection system with multi-dimensional integral imaging and ViT-GRU network outperforms other frame-based counterparts in degraded underwater environments. To the best of our knowledge, this is the first report on dynamic vision-based underwater optical signal detection using multidimensional integral imaging.