Oriented object detection in optical remote sensing images using deep learning: a survey
arXiv:2302.10473 · doi:10.1007/s10462-025-11256-0
Abstract
Oriented object detection is a fundamental yet challenging task in remote sensing (RS), aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods. Given the rapid development of this field, a comprehensive survey of the recent advances in oriented object detection is presented in this paper. Specifically, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific related challenges, including feature misalignment, spatial misalignment, oriented bounding box (OBB) regression problems, and common issues encountered in RS. Subsequently, we further categorize the existing methods into detection frameworks, OBB regression techniques, feature representation approaches, and solutions to common issues and provide an in-depth discussion of how these methods address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis involving the state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection research.
Wang, K., Wang, Z., Li, Z. et al. Oriented object detection in optical remote sensing images using deep learning: a survey. Artif Intell Rev 58, 350 (2025). https://doi.org/10.1007/s10462-025-11256-0
References in corpus (22)
- SSD: Single Shot MultiBox Detector
- Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
- A Survey on Object Detection in Optical Remote Sensing Images
- Arbitrary-Oriented Scene Text Detection via Rotation Proposals
- Gliding vertex on the horizontal bounding box for multi-oriented object detection
- DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
- What makes for effective detection proposals?
- Towards Large-Scale Small Object Detection: Survey and Benchmarks
- Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
- Anchor-free Oriented Proposal Generator for Object Detection
- SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
- Deep Learning for UAV-based Object Detection and Tracking: A Survey
- CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images
- Rotation equivariant vector field networks
- From Handcrafted to Deep Features for Pedestrian Detection: A Survey
- A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
- A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery
- ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection
- Focus-and-Detect: A Small Object Detection Framework for Aerial Images
- Adaptive Period Embedding for Representing Oriented Objects in Aerial Images
- PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
- Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images