  Underwater acoustic cameras are high potential devices for many applications
in ecology, notably for fisheries management and monitoring. However how to
extract such data into high value information without a time-consuming entire
dataset reading by an operator is still a challenge. Moreover the analysis of
acoustic imaging, due to its low signal-to-noise ratio, is a perfect training
ground for experimenting with new approaches, especially concerning Deep
Learning techniques. We present hereby a novel approach that takes advantage of
both CNN (Convolutional Neural Network) and classical CV (Computer Vision)
techniques, able to detect a generic class ''fish'' in acoustic video streams.
The pipeline pre-treats the acoustic images to extract 2 features, in order to
localise the signals and improve the detection performances. To ensure the
performances from an ecological point of view, we propose also a two-step
validation, one to validate the results of the trainings and one to test the
method on a real-world scenario. The YOLOv3-based model was trained with data
of fish from multiple species recorded by the two common acoustic cameras,
DIDSON and ARIS, including species of high ecological interest, as Atlantic
salmon or European eels. The model we developed provides satisfying results
detecting almost 80% of fish and minimizing the false positive rate, however
the model is much less efficient for eel detections on ARIS videos. The first
CNN pipeline for fish monitoring exploiting video data from two models of
acoustic cameras satisfies most of the required features. Many challenges are
still present, such as the automation of fish species identification through a
multiclass model. 1 However the results point a new solution for dealing with
complex data, such as sonar data, which can also be reapplied in other cases
where the signal-to-noise ratio is a challenge.
