Maritime object detection in synthetic aperture radar (SAR) imagery has seen a resurgence of interest due to the introduction of deep object detection models and large-scale datasets from competitions such as xView3. However, as novel examples are seen in the wild and as new SAR sensors emerge, existing models will need to be retrained with relevant new data. Active learning (AL) aims to automate and optimize this data curation process. In this work, we evaluate state-of-the-art AL algorithms for the task of SAR maritime object detection. Through analyzing and identifying gaps in current AL solutions we seek to motivate efforts to improve their utility in practical settings.
Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classification, especially when training data are abundantly available, as is the case with the ImageNet project. However, several researchers have exposed potential vulnerabilities of these networks to carefully crafted adversarial imagery. Additionally, researchers have shown the sensitivity of these networks to some types of noise and distortion. In this paper, we investigate the use of no-reference image quality metrics to identify adversarial imagery and images of poor quality that could potentially fool a deep learning network or dramatically reduce its accuracy. Results are shown on several adversarial image databases with comparisons to popular image classification databases.
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