[1]ZHAO Zhenbo,FU Tianyi,DONG Hongbin,et al.Decoupled feature mining rotational detector based on proposal enhancement[J].CAAI Transactions on Intelligent Systems,2025,20(5):1123-1135.[doi:10.11992/tis.202410017]
Copy

Decoupled feature mining rotational detector based on proposal enhancement

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1123-1135 Column: 学术论文—机器感知与模式识别 Public date: 2025-09-05
Title:
Decoupled feature mining rotational detector based on proposal enhancement
Author(s):
ZHAO Zhenbo1;  FU Tianyi1;  DONG Hongbin1;  ZHANG Xiaoping2
1. School of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2. Traditional Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
Keywords:
remote sensing image; object detection; rotated object detection; small object; high density; oriented bounding box; cross-scale fusion; two-stage detector
CLC:
TP391
DOI:
10.11992/tis.202410017
Abstract:
In remote sensing images, small and cluttered objects often appear intertwined, presenting considerable challenges for object detection. These challenges are even further amplified in rotational object detection tasks. Aiming to address these challenges, this paper proposes a decoupled feature mining rotational detector based on proposal enhancement (PDMDet). First, a single-stage detector is employed to replace the region proposal network of the two-stage detector, generating high-quality proposals to reduce background redundancy. Second, self-attention is applied within the same feature dimensions and cross-attention across different dimensions, aiming to enhance intradimensional features and fuse interdimensional features, thereby improving the capability of the detector to identify objects of varying sizes. Finally, recognizing that classification and oriented bounding box regression tasks have different feature sensitivities, this paper proposes a decoupled feature refinement strategy that processes the two tasks separately. Experiment results demonstrate that PDMDet achieves single-scale mAP scores of 78.37%, 72.35%, and 98.60% on DOTA-v1.0, DOTA-v1.5, and HRSC2016 datasets, respectively, outperforming existing algorithms in terms of detection accuracy and demonstrating strong competitiveness in complex rotational object detection scenarios.
References:
[1] 蓝鑫, 吴淞, 伏博毅, 等. 深度学习的遥感图像旋转目标检测综述[J]. 计算机科学与探索, 2024, 18(4): 861-877.
LAN Xin, WU Song, FU Boyi, et al. Survey on deep learning in oriented object detection in remote sensing images[J]. Journal of frontiers of computer science and technology, 2024, 18(4): 861-877.
[2] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[3] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[4] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer International Publishing, 2016: 21–37.
[5] 徐红明, 王兴华, 方诚, 等. 基于旋转不变性的高分辨率遥感影像船舶检测[J]. 中国航海, 2024, 47(2): 120-127.
XU Hongming, WANG Xinghua, FANG Cheng, et al. Rotation invariant object detection assisted ship identification from high-resolution remote sensing imagery[J]. Navigation of China, 2024, 47(2): 120-127.
[6] 牛为华, 郭迅. 基于改进YOLOv8的船舰遥感图像旋转目标检测算法[J]. 图学学报, 2024, 45(4): 726-735.
NIU Weihua, GUO Xun. Rotating target detection algorithm in ship remote sensing images based on YOLOv8[J]. Journal of graphics, 2024, 45(4): 726-735.
[7] TIAN Zhi, SHEN Chunhua, CHEN Hao, et al. FCOS: fully convolutional one-stage object detection[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 9626-9635.
[8] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 42(2): 318-327.
[9] YANG Ze, LIU Shaohui, HU Han, et al. RepPoints: point set representation for object detection[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 9656–9665.
[10] DING Jian, XUE Nan, LONG Yang, et al. Learning RoI Transformer for oriented object detection in aerial images[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 2844-2853.
[11] YANG Xue, YANG Jirui, YAN Junchi, et al. SCRDet: towards more robust detection for small, cluttered and rotated objects[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 8231-8240.
[12] HAN Jiaming, DING Jian, LI Jie, et al. Align deep features for oriented object detection[J]. IEEE transactions on geoscience and remote sensing, 2021, 60: 5602511.
[13] YANG Xue, YAN Junchi, FENG Ziming, et al. R3det: refined single-stage detector with feature refinement for rotating object[C]//Proceedings of the AAAI Conference on Artificial Intelligence. [S. l. ]: AAAI Press, 2021: 3163–3171.
[14] XIE Xingxing, CHENG Gong, WANG Jiabao, et al. Oriented R-CNN for object detection[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 3500-3509.
[15] DAI Linhui, LIU Hong, TANG Hao, et al. AO2-DETR: arbitrary-oriented object detection Transformer[J]. IEEE transactions on circuits and systems for video technology, 2023, 33(5): 2342-2356.
[16] 杨学, 严骏驰. 基于特征对齐和高斯表征的视觉有向目标检测[J]. 中国科学: 信息科学, 2023, 53(11): 2250-2265.
YANG Xue, YAN Junchi. Visual oriented object detection via feature alignment and Gaussian parameterization[J]. Scientia sinica (informationis), 2023, 53(11): 2250-2265.
[17] 涂可龙, 卿雅娴, 李真强, 等. EllipticNet: 基于椭圆方程的遥感有向目标检测[J]. 遥感学报, 2025, 29(3): 713-727.
TU Kelong, QING Yaxian, LI Zhenqiang, et al. EllipticNet: equation-based remote sensing oriented object detection network[J]. National remote sensing bulletin, 2025, 29(3): 713-727.
[18] 毛泽勇, 陈欣易, 丁俊峰, 等. 基于不确定性感知旋转目标检测的二次接线质检[J]. 计算机技术与发展, 2024, 34(10): 178-185.
MAO Zeyong, CHEN Xinyi, DING Junfeng, et al. Uncertainty-aware oriented object detection for trustworthy quality inspection of secondary wiring[J]. Computer technology and development, 2024, 34(10): 178-185.
[19] HAN Jiaming, DING Jian, XUE Nan, et al. ReDet: a rotation-equivariant detector for aerial object detection[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. [S. l. ]: IEEE, 2021: 2786-2795.
[20] FENG Xiaoxu, YAO Xiwen, CHENG Gong, et al. Weakly supervised rotation-invariant aerial object detection network[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 14126-14135.
[21] 方婷婷, 刘斌, 陈春晖, 等. 视图一致性网络下的弱监督遥感影像旋转目标检测[J]. 遥感学报, 2024, 28(12): 3213-3230.
FANG Tingting, LIU Bin, CHEN Chunhui, et al. View-consistency network for weakly supervised oriented object detection in remote sensing images[J]. National remote sensing bulletin, 2024, 28(12): 3213-3230.
[22] ZHOU Yanzhao, YE Qixiang, QIU Qiang, et al. Oriented response networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4961-4970.
[23] ALEXEY D, LUCAS B, ALEXANDER K, et al. An image is worth 16x16 words Transformers for image recognition at scale[C]//International Conference on Learning Representations. [S. l. ]: OpenReview.net, 2021: 1-21.
[24] LIU Ze, LIN Yutong, CAO Yue, et al. Swin Transformer: hierarchical vision Transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 9992-10002.
[25] WU Yuhuan, LIU Yun, ZHAN Xin, et al. P2T: pyramid pooling Transformer for scene understanding[J]. IEEE transactions on pattern analysis and machine intelligence, 2023, 45(11): 12760-12771.
[26] ZHOU Xingyi, KOLTUN V, KR?HENBüHL P. Probabilistic two-stage detection[EB/OL]. (2021-3-12)[2024-11-21]. https://arxiv.org/abs/2103.07461v1.
[27] KIM K, LEE H S. Probabilistic anchor assignment with IoU prediction for object detection[C]//European Conference on Computer Vision. Cham: Springer International Publishing, 2020: 355-371.
[28] 付天怡, 杨本翼, 董红斌, 等. 基于深度重塑的航拍目标检测增强网络[J]. 模式识别与人工智能, 2024, 37(7): 652-662.
FU Tianyi, YANG Benyi, DONG Hongbin, et al. Depth-reshaping based aerial object detection enhanced network[J]. Pattern recognition and artificial intelligence, 2024, 37(7): 652-662.
[29] ASHISH V, NOAM S, NIKI P, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems. Long Beach: Springer, 2017: 1-11.
[30] ZHOU Yue, YANG Xue, ZHANG Gefan, et al. MMRotate: a rotated object detection benchmark using PyTorch[C]//Proceedings of the 30th ACM International Conference on Multimedia. Lisboa: ACM, 2022: 7331–7334.
[31] XIA Guisong, BAI Xiang, DING Jian, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3974-3983.
[32] LIU Zikun, YUAN Liu, WENG Lubin, et al. A high resolution optical satellite image dataset for ship recognition and some new baselines[C]//Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. Porto: SCITEPRESS-Science and Technology Publications, 2017: 324-331.
[33] LI Yuxuan, HOU Qibin, ZHENG Zhaohui, et al. Large selective kernel network for remote sensing object detection[C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 16748-16759.
[34] YANG Xue, YAN Junchi, MING Qi, et al. Rethinking rotated object detection with Gaussian Wasserstein distance loss[C]//International Conference on Machine Learning. [S. l. ]: PMLR, 2021: 11830–11841.
[35] YANG Xue, YANG Xiaojiang, YANG Jirui, et al. Learning high-precision bounding box for rotated object detection via kullback-leibler divergence[C]//Advances in Neural Information Processing Systems. [S. l. ]: Curran Associates Inc., 2021: 18381–18394.
[36] PU Yifan, WANG Yiru, XIA Zhuofan, et al. Adaptive rotated convolution for rotated object detection[C]//2023 IEEE/CVF International Conference on Computer Vision. Paris: IEEE, 2023: 6566-6577.
[37] WANG Jiangshan, PU Yifan, HAN Yizeng, et al. GRA: detecting oriented objects through group-wise rotating and attention[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 298-315.
[38] CAI Xinhao, LAI Qiuxia, WANG Yuwei, et al. Poly kernel inception network for remote sensing detection[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2024: 27706-27716.
[39] HE Kaiming, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988.
[40] CHEN Kai, PANG Jiangmiao, WANG Jiaqi, et al. Hybrid task cascade for instance segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4969-4978.
[41] LI Wentong, CHEN Yijie, HU Kaixuan, et al. Oriented RepPoints for aerial object detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 1819-1828.
[42] EVERINGHAM M, VAN G, WILLIAMS C K I, et al. The PASCAL visual object classes challenge 2007 (VOC2007) results[EB/OL]. (2007-06-07)[2024-12-20]. http://www.pascalnetwork.org/challenges/VOC/voc2007/workshop/index.html.
[43] EVERINGHAM M, VAN G, WILLIAMS C K I, et al. The PASCAL visual object classes challenge 2012 (VOC2012) results[EB/OL]. (2012-05-18)[2024-12-20]. http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html.
[44] KOLEN J F, KREMER S C. Gradient flowin recurrent nets: the difficulty of learning longterm dependencies[J]. A field guide to dynamical recurrent networks, 2001, 237-243.
Similar References:

Memo

-

Last Update: 2025-09-05

Copyright © CAAI Transactions on Intelligent Systems