[1] Xue J D, Zhu J J, Zhang J et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 42, 1210002(2022).

[2] Nong Y J, Wang J J. Real-time object detection in remote sensing images based on embedded system[J]. Acta Optica Sinica, 41, 1028001(2021).

[3] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

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

[5] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[6] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[7] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[8] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

[9] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[10] Ren S Q, He K M, 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, 39, 1137-1149(2017).

[11] Yang X, Yang J R, Yan J C et al. SCRDet: towards more robust detection for small, cluttered and rotated objects[C], 8231-8240(2019).

[12] Guo H Y, Yang X, Wang N N et al. A rotational libra R-CNN method for ship detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 5772-5781(2020).

[13] Xue F F, Wang Y M, Li Q. Recognition of cattle daily behavior based on spatial relationship of feature parts[J]. Laser & Optoelectronics Progress, 58, 2215007(2021).

[14] Zhang H, Zu K K, Lu J et al. EPSANet: an efficient pyramid split attention block on convolutional neural network[EB/OL]. https://arxiv.org/abs/2105.14447

[15] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

[16] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[17] Yang X, Hou L P, Zhou Y et al. Dense label encoding for boundary discontinuity free rotation detection[C], 15814-15824(2021).

[18] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

[19] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[20] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[21] Tan M X, Pang R M, Le Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(2020).

[22] Yang X, Yan J C. Arbitrary-oriented object detection with circular smooth label[M]. Vedaldi A, Bischof H, Brox T, et al. Computer Vision-ECCV 2020. Lecture notes in computer science, 12353, 677-694(2020).

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

[24] Lin Y T, Feng P M, Guan J et al. IENet: interacting embranchment one stage anchor free detector for orientation aerial object detection[EB/OL]. https://arxiv.org/abs/1912.00969v2

[25] 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).

[26] Jiang Y Y, Zhu X Y, Wang X B et al. R2CNN: rotational region CNN for arbitrarily-oriented scene text detection[C], 3610-3615(2018).

[27] 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).

[28] Xu Y C, Fu M T, Wang Q M et al. Gliding vertex on the horizontal bounding box for multi-oriented object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1452-1459(2021).