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DIDSON acoustic cameras provide a way to collect temporally dense, high-resolution imaging data, similar to videos. Detection of fish targets on those videos takes place in a manual or semi-automated manner, typically assisted by specialised software. Exploiting the visual nature of the recordings, tools and techniques from the field of computer vision can be applied in order to facilitate the relatively involved workflows. Furthermore, machine learning techniques can be used to minimise user intervention and optimise for specific detection and tracking scenarios. This study explored the feasibility of combining optical flow with a genetic algorithm, with the aim of automating motion detection and optimising target-to-background segmentation (masking) under custom criteria, expressed in terms of the result. A 1000-frame video sequence sample with sparse, smoothly moving targets, reconstructed from a 125 s DIDSON recording, was analysed under two distinct scenarios, and an elementary detection method was used to assess and compare the resulting foreground (target) masks. The results indicate a high sensitivity to motion, as well as to the visual characteristics of targets, with the resulting foreground masks generally capturing fish targets on the majority of frames, potentially with small gaps of undetected targets, lasting for no more than a few frames. Despite the high computational overhead, implementation refinements could increase computational feasibility, while an extension of the algorithms, in order to include the steps of target detection and tracking, could further improve automation and potentially provide an efficient tool for the automated preliminary assessment of voluminous DIDSON data recordings.

Optimising the Workflow for Fish Detection in DIDSON (Dual-Frequency IDentification SONar) Data with the Use of Optical Flow and a Genetic Algorithm