ASL-OOD: Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection
Source abstract
Abstract
Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection. Current frameworks for oriented detection modules are constrain... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Image Retrieval and Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
Show source-provided references
1. SCRDet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing10.1109/TPAMI.2022.3166956 ↗
2. Y. Jiang et al., “R2CNN: Rotational region CNN for orientation robust scene text detection,” 2017, arXiv:1706.09579.10.1109/ICPR.2018.8545598 ↗
3. R. Girshick, “Fast R-CNN,” 2015, arXiv:1504.08083.10.1109/ICCV.2015.169 ↗
4. You only look once: Unified, real-time object detection
5. A. Wang et al., “YOLOv10: Real-time end-to-end object detection,” 2024, arXiv:2405.14458.
6. End-to-end object detection with transformers
7. Segment anything
8. Arbitrary-oriented scene text detection via rotation proposals10.1109/TMM.2018.2818020 ↗
9. J. Ding, N. Xue, Y. Long, G. -S. Xia, and Q. Lu, “Learning RoI transformer for detecting oriented objects in aerial images,” 2018, arXiv:1812.00155.10.1109/CVPR.2019.00296 ↗
10. SANet: Structure-aware network for visual tracking
11. RepPoints: Point set representation for object detection
12. Object detection in 20 years: A survey10.1109/JPROC.2023.3238524 ↗
13. SCRDet: Towards more robust detection for small, cluttered and rotated objects
14. Learning RoI transformer for oriented object detection in aerial images
15. Gliding vertex on the horizontal bounding box for multi-oriented object detection10.1109/TPAMI.2020.2974745 ↗
16. R3Det: Refined single-stage detector with feature refinement for rotating object
17. Oriented object detection in aerial images with box boundary-aware vectors
18. C2-YOLO: Rotating object detection network for remote sensing images with complex backgrounds
19. Align deep features for oriented object detection
20. Dynamic anchor learning for arbitrary-oriented object detection
21. Dynamic refinement network for oriented and densely packed object detection
22. Swin transformer: Hierarchical vision transformer using shifted windows
23. Feature pyramid networks for object detection
24. Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection10.1109/TIP.2021.3118953 ↗
25. Learning modulated loss for rotated object detection
26. Z. Ge, “YOLOX: Exceeding YOLO series in 2021,” 2021, arXiv:2107.08430.
27. DOTA: A large-scale dataset for object detection in aerial images
28. Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds10.1109/LGRS.2016.2565705 ↗
29. MAR20: A benchmark for military aircraft recognition in remote sensing images
30. The unmanned aerial vehicle benchmark: Object detection and tracking
31. PIoU loss: Towards accurate oriented object detection in complex environments
32. ultralytics/yolov5: v5.0-YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations
33. R-YOLO: A YOLO-based method for arbitrary-oriented target detection in high-resolution remote sensing images10.3390/s22155716 ↗
34. Enhancing remote sensing object detection with K-CBST YOLO: Integrating CBAM and swin-transformer10.3390/rs16162885 ↗
35. Mask OBB: A semantic attention-based mask oriented bounding box representation for multi-category object detection in aerial images10.3390/rs11242930 ↗
36. FAOD-Net: A fast AOD-Net for dehazing single image10.1155/2020/4945214 ↗
37. Point-based estimator for arbitrary-oriented object detection in aerial images10.1109/TGRS.2020.3020165 ↗
38. Z. Tian, C. Shen, H. Chen, and T. He, “Fully convolutional one-stage object detection,” 2019, arXiv:1904.01355.10.1109/ICCV.2019.00972 ↗
39. J. Redmon, “YOLOv3: An incremental improvement,” 2018, arXiv:1804.02767.
40. T. Lin, “Focal loss for dense object detection,” 2017, arXiv:1708.02002.10.1109/ICCV.2017.324 ↗
41. A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” 2020, arXiv:2004.10934.
42. An improved lightweight real-time detection algorithm based on the edge computing platform for UAV images10.3390/electronics12102274 ↗
43. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
44. G. Yu et al., “PP-PicoDet: A better real-time object detector on mobile devices,” 2021, arXiv:2111.00902.
45. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
46. UAV-YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios
47. Oriented R-CNN for object detection
48. LWUAVDet: A lightweight UAV object detection network on edge devices10.1109/JIOT.2024.3388045 ↗
49. HLA: Harmonized label assigner for two-stage oriented object detection