Seabed characterization is critical for mine countermeasures planning and evaluation, and this study extends prior efforts addressing it as deep learning segmentation with synthetic aperture sonar data. Although traditional crisp annotations have …
Seabed characterization is critical for mine countermeasures planning and evaluation, and this study extends prior efforts addressing it as deep learning segmentation with synthetic aperture sonar data. Although traditional crisp annotations have …
This paper addresses a critical issue in seabed characterization with deep learning semantic segmentation using high-resolution Synthetic Aperture Sonar (SAS) data, that we call Catastrophic Receptive Field Overflow (CRFO). We propose novel methods, …
Medical images are often of huge size, which presents a challenge in terms of memory requirements when training machine learning models. Commonly, the images are downsampled to overcome this challenge, but this leads to a loss of information. We …
We explore the potential of deep learning in digital painting analysis to facilitate condition reporting and to support restoration treatments. We address the problem of paint loss detection and develop a multiscale deep learning system with dilated …