Forward-Looking Sonar CNN-based Automatic Target Recognition: an experimental campaign with FeelHippo AUV
Leonardo Zacchini, Matteo Franchi, Vincenzo Manzari, Marco Pagliai, Nicola Secciani, Alberto Topini, Mirko Stifani, Alessandro Ridolfi
Abstract
In recent years, seabed inspection has become one of the most sought-after tasks for Autonomous Underwater Vehicles (AUVs) in various applications. Forward-Looking Sonars (FLS) are commonly favored over optical cameras, which are not-negligibly affected by environmental conditions, to carry out inspection and exploration tasks. Indeed, sonars are not influenced by illumination conditions and can provide high-range data at the cost of lower-resolution images compared to optical sensors. However, due to the lack of features, sonar images are often hard to interpret by using conventional image processing techniques, leading to the necessity of human operators analyzing thousands of images acquired during the AUV mission to identify the potential targets of interest. This paper reports the development of an Automatic Target Recognition (ATR) methodology to identify and localize potential targets in FLS imagery, which could help human operators in this challenging, demanding task. The Mask R-CNN (Region-based Convolutional Neural Network), which constitutes the core of the proposed solution, has been trained with a dataset acquired in May 2019 at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale - CSSN) basin, in La Spezia (Italy). The ATR strategy was then successfully validated online in the same site in October 2019, where the targets were replaced and relocated on the seabed.