From Event: SPIE Defense + Commercial Sensing, 2023
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyle L. Hom, Joseph M. Cuschieri, Michael S. Sokoloff, and Oliver Fritsche, "Underwater target detection, localization, and classification," Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210I (Presented at SPIE Defense + Commercial Sensing: May 02, 2023; Published: 13 June 2023); https://doi.org/10.1117/12.2663172.