Instance Segmentation-Based Identification of Pelagic Species in Acoustic Backscatter Data
This paper addresses the automatic identification of pelagic species in acoustic backscatter data. Large quantities of data acquired during underwater acoustic surveys for environmental monitoring and resources management, visualized as echograms, are typically analyzed manually or semi-automatically by marine biologists, which is time-consuming and prone to errors and inter-expert disagreements. In this paper, we propose to detect pelagic species (schools of herring and of juvenile salmon) from echograms with a deep learning (DL) framework based on instance segmentation, allowing us to carefully study the acoustic properties of the targets and to address specific challenges such as close proximity between schools and varying size. Experimental results demonstrate our system's ability to correctly detect pelagic species from echograms and to outperform an existing object detection framework designed for schools of herring in terms of detection performance and computational resources utilization. Our pixel-level detection method has the advantage of generating a precise identification of the pixel groups forming each detection, opening up many possibilities for automatic biological analyses.