Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey

Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection. Given the rapid development of this field, this paper presents a comprehensive survey of recent advances in oriented object detection. To be specific, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific challenges, including feature misalignment, spatial misalignment, and oriented bounding box (OBB) regression problems. Subsequently, we further categorize existing methods into detection framework, OBB regression, and feature representations, and provide an in-depth discussion on how these approaches address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis of state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection.

@article{wang2025_2302.10473,
  title={ Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey },
  author={ Kun Wang and Zi Wang and Zhang Li and Ang Su and Xichao Teng and Erting Pan and Minhao Liu and Qifeng Yu },
  journal={arXiv preprint arXiv:2302.10473},
  year={ 2025 }
}