Sonar Point Cloud Processing to Identify Sea Turtles by Pattern Analysis

TL;DR

This paper presents a novel pattern analysis-based sonar detection method for identifying sea turtles in point cloud data, overcoming low signal-to-clutter ratios caused by reverberations, and validated through simulations and sea tests.

Contribution

The paper introduces an unsupervised clustering and classification approach for sea turtle detection in sonar point clouds, addressing low SCR challenges with spectral and geometric features.

Findings

Robust detection at low SCR levels

Effective clustering of reflection patterns

Successful sea turtle detection in real and simulated environments

Abstract

Abundant in coastal areas, sea turtles are affected by high-intensity acoustic anthropogenic sounds. In this paper, we offer a pattern analysis-based detection approach to serve as a warning system for the existence of nearby sea turtles. We focus on the challenge of overcoming the low signal-to-clutter ratio (SCR) caused by reverberations. Assuming that, due to low SCR, target reflections within the point cloud are received in groups, our detector searches for patterns through clustering to identify possible 'blobs' in the point cloud of reflections, and to classify them as either clutter or a target. Our unsupervised clustering is based on geometrical and spectral constraints over the blob's member relations. In turn, the classification of identified blobs as either a target or clutter is based on features extracted from the reflection pattern. To this end, assuming reflections from a sea turtle are stable but include spectral diversity due to distortions within the turtles body, we quantify the stability of the blob's members and their spectral entropy. We test our detector in both modeled simulations, and at sea, for the detection of sea turtles released after rehabilitation. The results show robustness to highly-fluctuating target intensity and ability to detect at low SCR.