Detecting ships in synthetic aperture radar (SAR) images poses a formidable challenge,
primarily attributed to limited observation samples and complex environments. To address …

Due to the complex imaging mechanism of SAR images and the lack of multi-angle and
multi-parameter real scene SAR target data, the generalization performance of existing deep …

Fast and accurate object detection in foggy weather is crucial for visual tasks such as
autonomous driving and video surveillance. Existing methods typically preprocess images …

Federated Learning (FL) has emerged as a potent framework for training models across
distributed data sources while maintaining data privacy. Nevertheless, it faces challenges …

The utilization of deep learning methods for the detection of space targets and components
has received significant attention due to the continuous development of space missions …

Addressing the challenge of domain shift between datasets is vital in maintaining model
performance. In the context of cross-domain object detection, the teacher–student …

The development of machine learning-based maritime object detection technology aims to
assist ship operators in maritime surveillance. However, as maritime environments can be …

For maritime autonomous surface ships, challenges exist in visual detection of ships in sea
foggy scenarios, thereby severely degrading visual detection autonomy. In this paper, math …

In practice, object detection models used for construction site monitoring exhibit
performance degradation owing to different monitoring settings and dynamic construction …

Unsupervised domain adaptation (UDA) is critical for remote sensing object detection in real
applications, aiming to address the significant performance degradation issue caused by the …