Candidate: Martin Joseph Aubard
Date, time and location:
25 July 2025, 14:00, Sala de Atos DEEC – I-105, Faculty of Engineering, University of Porto
President of the Jury:
Pedro Nuno Ferreira da Rosa da Cruz Diniz (PhD), Full Professor, Department of Informatics Engineering, Faculty of Engineering, University of Porto
Members:
Bilal Wehbe, Senior Researcher at the German Research Center for Artificial Intelligence, Germany;
Catarina Helena Branco Simões da Silva, Associate Professor, Department of Computer Engineering, Faculty of Science and Technology, University of Coimbra;
Andry Maykol Gomes Pinto, Associate Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto;
Ana Maria Dias Madureira Pereira, Coordinating Professor with Aggregation, Department of Computer Engineering, Instituto Superior de Engenharia do Porto, Polytechnic of Porto (Supervisor).
The thesis was co-supervised by Luís Filipe Pinto de Almeida Teixeira (PhD), Associate Professor in the Department of Informatics Engineering at the Faculty of Engineering of the University of Porto.
Abstract:
This thesis addresses the challenge of improving Autonomous Underwater Vehicles (AUVs) onboard detection and interaction capabilities using Side-Scan Sonar (SSS) data. Traditionally, underwater missions relied on pre-defined plans where data are analyzed post-mission by operators or experts. This workflow is time-consuming, often requiring multiple missions to identify and localize underwater targets. The need for repeated missions increases operational costs and complexity, highlighting the inefficiency of current methodologies. Moreover, such approaches do not allow the AUV to interact with detected targets in real time, limiting the scope of mission adaptation and real-time decision-making. To overcome these limitations, this thesis presents a novel framework integrating deep learning models for object detection directly onboard AUVs. This integration enables the vehicle to detect, localize, and interact with underwater targets in real time, offering significant improvements over traditional post-mission analysis. The framework builds upon the LSTS toolchain, which is responsible for AUV motion control and communication, and introduces enhanced real-time data processing capabilities. However, implementing such a model into an embedded system suffers from computational limitations affecting the model’s performance. Thus, the knowledge distillation methods have been implemented, ensuring smaller, more efficient models to perform onboard detection without sacrificing accuracy. Additionally, to improve the model’s robustness against underwater noise, a novel adversarial retraining framework, ROSAR, is introduced, ensuring reliable operation even in noisy sonar environments. Following the onboard detection and localization enhancement, we focused on onboard interaction with the detected object. This is realized by extending the previous onboard framework and validating it through a customized simulator, enhancing interaction with the detected objects, and validating through a pipeline inspection use case, which reduces mission time by combining sonar detection and camera data collection in a single mission, utilizing behavior trees and safety-assessed models. Given the lack of open-source sonar datasets in the field, this thesis contributes to two novel publicly available side-scan sonar datasets, SWDD and Subpipe, which include field-collected data on walls and pipelines and are manually annotated for object detection. By shifting from post-mission analysis to real-time detection and interaction, this thesis significantly improves the operational efficiency of AUV missions. The proposed framework streamlines underwater operations and enhances AUVs’ autonomous behavior, relying on efficient, accurate, and robust object detection model for efficient underwater exploration and monitoring applications.