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Oriented object detection in aerial images has made significant advancements propelled by well-developed detection frameworks and diverse representation approaches to oriented bounding boxes. However, ...
Oriented object detection in remote sensing images (RSIs) relies heavily on costly annotated data. To alleviate this challenge, we propose a straightforward yet powerful approach for semi-supervised ...
Oriented object detection, which aims at detecting objects with orientation properties, shows great potential for visual analysis in complex scenarios, such as aerial images. However, the powerful ...
To ensure long-term space missions, an autonomous localization and mapping system for lunar rovers is demanded. While the target-oriented localization and mapping problem can be solved through ...
In addition, there needs to be more research on applying deep learning technology in education. In this article, we develop an intelligent agent using a performer-based encoder–decoder neural model to ...
Object detection is a fundamental task in remote sensing image analysis and scene understanding. Previous remote sensing object detectors are typically based on convolutional neural networks (CNNs), ...
Furthermore, the complex background and dense distribution of objects in remote sensing images also increase the difficulty of detection, making it vulnerable to noise and fuzzy boundary interference.
Dual quadrics as landmarks in object-oriented SLAM have recently attracted much attention due to the advantages in the mathematical completeness of projective geometry. Current researches suffer from ...
Arbitrary-oriented object detection (AOOD) is a challenging task to detect objects in the wild with arbitrary orientations and cluttered arrangements. Existing approaches are mainly based on ...