Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

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

Annotating remote sensing images (RSIs) poses a significant challenge, primarily due to its
labor-intensive nature. Semi-supervised object detection (SSOD) methods address this …

Despite the remarkable progress of general object detection, the lack of labeled aerial
images limits the robustness and generalization of the detector. Teacher–student learning is …

As the development of self-driving (autonomous) vehicles is getting matured and ready for
the road, there is a growing concern regarding their safety performance among the general …

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 …

To address the different forms and sizes of bearing collar surface defects, uneven
distribution of defect positions, and complex backgrounds, we propose ESD-YOLOv5, an …

Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR)
images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A …

In industrial detection scenarios, achieving high accuracy typically relies on extensive
labeled datasets, which are costly and time-consuming. This has motivated a shift towards …

3D object detection aims to recover the 3D information of concerning objects and serves as
the fundamental task of autonomous driving perception. Its performance greatly depends on …