Underwater target detection, localization, and classification

• Main Authors: Hom, Kyle L., Cuschieri, Joseph M., Sokoloff, Michael S., Fritsche, Oliver
• Format: Conference Proceeding
• Language: English
• Published: SPIE 13.06.2023
• ISBN: 1510661565; 9781510661561
• ISSN: 0277-786X
• DOI: 10.1117/12.2663172

Detection of underwater manmade objects via deep learning solutions is subject to unique challenges particular to sonar imaging, such as sparse target examples on the seafloor, distortions from sonar image construction, and variations of target pose and sediment accumulation. The combined effect of these artifacts in underwater imagery reduces the efficacy of convolutional neural networks, which achieve state-of-the-art performance on photometric imagery. To mitigate these issues, we implement synthetic data generation via factorization of StyleGAN2 and distortion augmentation, and evaluate several object detection architectures. We develop a detection framework to integrate these treatments and present results on a synthetic aperture sonar imagery dataset collected by an uncrewed undersea vehicle.