PNNL

Abstract

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. Here, we describe integrating deep learning with a non-lethal, non-intrusive imaging method – digital holography – to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. . The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at powerplant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.