[1] Wang K, Wang Z, Li Z et al. Oriented object detection in optical remote sensing images using deep learning: a survey[EB/OL]. http://arxiv.org/abs/1909.00133

[2] Ding J, Xue N, Long Y et al. Learning RoI Transformer for oriented object detection in aerial images[C], 2844-2853(2019).

[3] Ma J Q, Shao W Y, Ye H et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Transactions on Multimedia, 20, 3111-3122(2018).

[4] Han J M, Ding J, Xue N et al. ReDet: a rotation-equivariant detector for aerial object detection[C], 2785-2794(2021).

[5] Xie X X, Cheng G, Wang J B et al. Oriented R-CNN for object detection[C], 3500-3509(2021).

[6] Yang X, Yan J C, Feng Z M et al. R3Det: refined single-stage detector with feature refinement for rotating object[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 3163-3171(2021).

[7] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[8] Han J M, Ding J, Li J et al. Align deep features for oriented object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5602511(2048).

[9] Carion N, Massa F, Synnaeve G, Vedaldi A, Bischof H, Brox T et al. End-to-end object detection with Transformers[M]. Computer vision-ECCV 2020. Lecture notes in computer science, 12346, 213-229(2020).

[10] Zhu X Z, Su W J, Lu L W et al. Deformable DETR: deformable Transformers for end-to-end object detection[EB/OL]. http://arxiv.org/abs/2010.04159

[11] Liu S L, Li F, Zhang H et al. DAB-DETR: dynamic anchor boxes are better queries for DETR[EB/OL]. http://arxiv.org/abs/2201.12329

[12] Li F, Zhang H, Liu S L et al. DN-DETR: accelerate DETR training by introducing query DeNoising[C], 13609-13617(2022).

[13] Zhang H, Li F, Liu S L et al. DINO: DETR with improved DeNoising anchor boxes for end-to-end object detection[EB/OL]. http://arxiv.org/abs/2203.03605

[14] Ma T L, Mao M Y, Zheng H H et al. Oriented object detection with Transformer[EB/OL]. http://arxiv.org/abs/2106.03146

[15] Dai L H, Liu H, Tang H et al. AO2-DETR: arbitrary-oriented object detection Transformer[J]. IEEE Transactions on Circuits and Systems for Video Technology, 33, 2342-2356(2023).

[16] Zeng Y, Yang X, Li Q Y et al. ARS-DETR: aspect ratio sensitive oriented object detection with Transformer[EB/OL]. http://arxiv.org/abs/2303.04989

[17] Yang X, Zhou Y, Zhang G F et al. The KFIoU loss for rotated object detection[EB/OL]. http://arxiv.org/abs/2201.12558

[18] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth16×16 words: Transformers for image recognition at scale[EB/OL]. http://arxiv.org/abs/2010.11929

[19] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 6000-6010(2017).

[20] Yang X, Yan J C, Liao W L et al. SCRDet++: detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 2384-2399(2023).

[21] Qian W, Yang X, Peng S L et al. Learning modulated loss for rotated object detection[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 2458-2466(2021).

[22] Yang X, Yan J C, Ming Q et al. Rethinking rotated object detection with Gaussian Wasserstein distance loss[EB/OL]. http://arxiv.org/abs/2101.11952

[23] Yang X, Zhang G F, Yang X J et al. Detecting rotated objects as Gaussian distributions and its 3-D generalization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 4335-4354(2023).

[24] Qian W, Yang X, Peng S L et al. RSDet: point-based modulated loss for more accurate rotated object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 7869-7879(2022).

[25] Yang X, Yang X J, Yang J R et al. Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence[EB/OL]. http://arxiv.org/abs/2106.01883

[26] Xia G S, Bai X, Ding J et al. DOTA: a large-scale dataset for object detection in aerial images[C], 3974-3983(2018).

[27] Cheng G, Wang J B, Li K et al. Anchor-free oriented proposal generator for object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5625411(1809).

[28] Lin T Y, Maire M, Belongie S, Fleet D, Pajdla T, Schiele B et al. Microsoft COCO: common objects in context[M]. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 740-755(2014).

[29] Wu Z Y, Suresh K, Narayanan P et al. Delving into robust object detection from unmanned aerial vehicles: a deep nuisance disentanglement approach[C], 1201-1210(2019).

[30] Li Z, Li X, Yang L F et al. Curriculum temperature for knowledge distillation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 1504-1512(2023).