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

• URL: http://arxiv.org/abs/2302.10473v4
• Date: Tue, 9 Apr 2024 05:47:57 GMT
• Title: Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
• Authors: Kun Wang, Zi Wang, Zhang Li, Ang Su, Xichao Teng, Minhao Liu, Qifeng Yu,
• Abstract summary: Oriented object detection is one of the most fundamental and challenging tasks in remote sensing. Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
• Score: 10.665235711722076
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 years have witnessed remarkable progress in oriented object detection using deep learning techniques. Given the rapid development of this field, this paper aims to provide a comprehensive survey of recent advances in oriented object detection. To be specific, we first review the technical evolution from horizontal object detection to oriented object detection and summarize the specific challenges, including feature misalignment, spatial misalignment, and periodicity of angle. Subsequently, we further categorize existing methods into detection framework, oriented bounding box (OBB) regression, and feature representations, and discuss how these methods address the above challenges in detail. In addition, we cover several publicly available datasets and performance evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis of state-of-the-art oriented object detection methods. Toward the end of this paper, we discuss several future directions for oriented object detection.

Related papers

• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
  We discuss the recent advances in deep learning-based object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T14:44:22Z)
• Few-Shot Object Detection: Research Advances and Challenges [15.916463121997843]
  Few-shot object detection (FSOD) combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years.
  arXiv  Detail & Related papers  (2024-04-07T03:37:29Z)
• Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances [51.70835702029498]
  This review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods. We identify five main challenges in RSOD, including multi-scale object detection, rotated object detection, weak object detection, tiny object detection, and object detection with limited supervision. We also review the widely used benchmark datasets and evaluation metrics within the field of RSOD, as well as the application scenarios for RSOD.
  arXiv  Detail & Related papers  (2023-09-13T06:48:32Z)
• A Comprehensive Study on Object Detection Techniques in Unconstrained Environments [0.0]
  Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-11T15:45:03Z)
• A Comparative Review of Recent Few-Shot Object Detection Algorithms [0.0]
  Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem. Recent studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions.
  arXiv  Detail & Related papers  (2021-10-30T07:57:11Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)
• Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [8.442460766094674]
  Object pose detection and tracking has attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Deep learning is the most promising one that has shown better performance than others. This paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
  arXiv  Detail & Related papers  (2021-05-29T12:59:29Z)
• Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
  Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications. Learning highly accurate models relies on the availability of datasets with a large number of annotated images. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
  arXiv  Detail & Related papers  (2021-05-27T23:34:06Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.