[1] Yu W Q, Cheng G, Wang M J et al. MAR20: a benchmark for military aircraft recognition in remote sensing images[J]. Nat Remote Sens Bull, 27, 2688-2696(2023).

[2] Liang L M, Chen K Q, Wang C B et al. Remote sensing image detection algorithm integrating visual center mechanism and parallel patch perception[J]. Opto-Electron Eng, 51, 240099(2024).

[3] Xiao Z J, Zhang J H, Lin B H. Feature coordination and fine-grained perception of small targets in remote sensing images[J]. Opto-Electron Eng, 51, 240066(2024).

[4] Dang Y L, Ye C X. A lightweight remote sensing image military aircraft detection model based on Faster R-CNN[J]. Laser J, 45, 111-117(2024).

[5] Ren S, He K, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 39, 1137-1149(2017).

[6] Sha M M, Li Y, Li A. Multiscale aircraft detection in optical remote sensing imagery based on advanced Faster R-CNN[J]. Nat Remote Sens Bull, 26, 1624-1635(2022).

[7] Liu Y Y, Liu C Y, Zhou S H et al. Aircraft target detection algorithm based on optimized faster RCNN remote sensing images[J/OL]. Mech Electr Eng Technol(2024). https://doi.org/10.3969/j.issn.1009-9492.2024.00127

[8] Carion N, Massa F, Synnaeve G et al. End-to-end object detection with transformers[C], 213-229(2020). https://doi.org/10.1007/978-3-030-58452-8_13.

[9] Dang S H, Li X Z, Xia Z Q et al. Research on open-set object detection in remote sensing images based on adaptive pre-screening[J]. J Electron Inf Technol, 46, 3908-3917(2024).

[10] Zhou X Y, Wang D Q, Krähenbühl P. Objects as points[Z](2019). https://arxiv.org/abs/1904.07850

[11] Li J, Zhou S, Zhu X C et al. Remote sensing image aircraft target detection combined with multiple channel attention[J]. Comput Eng Appl, 58, 209-217(2022).

[12] Huang Z H, Rui J, Lin Y Z et al. Aircraft target detection based on improved YOLOv5 in remote sensing imagery[J]. Bull Surv Mapp, 73-78,89(2024).

[13] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[C], 3-19(2018). https://doi.org/10.1007/978-3-030-01234-2_1.

[14] Wang J, Zhang S, Zhang Y et al. Improved YOLOv5's military aircraft detection algorithm[J]. Radio Eng, 54, 589-596(2024).

[15] Liu L, Zhang S, Bai Y A et al. Improved lightweight military aircraft detection algorithm of YOLOv8[J]. Comput Eng Appl, 60, 114-125(2024).

[16] Xu X Z, Jiang Y Q, Chen W H et al. Damo-YOLO: a report on real-time object detection design[Z](2022). https://arxiv.org/abs/2211.15444

[17] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 7464-7475(2023). https://doi.org/10.1109/CVPR52729.2023.00721.

[18] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2015). https://doi.org/10.1109/CVPR.2016.90.

[19] Ding X H, Zhang X Y, Ma N N et al. RepVGG: making VGG-style ConvNets great again[C], 13728-13737(2021). https://doi.org/10.1109/CVPR46437.2021.01352.

[20] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020). https://doi.org/10.1109/CVPR42600.2020.00165.

[21] Tian Z, Shen C H, Chen H et al. FCOS: fully convolutional one-stage object detection[C], 9626-9635(2019). https://doi.org/10.1109/ICCV.2019.00972.

[22] Chen Z X, He Z W, Lu Z M. DEA-Net: single image dehazing based on detail-enhanced convolution and content-guided attention[J]. IEEE Trans Image Process, 33, 1002-1015(2024).

[23] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[C], 12993-13000(2020). https://doi.org/10.1609/aaai.v34i07.6999.

[24] Zhang H, Zhang S J. Shape-IoU: more accurate metric considering bounding box shape and scale[Z](2023). https://arxiv.org/abs/2312.17663

[25] Tong Z J, Chen Y H, Xu Z W et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[Z](2023). https://arxiv.org/abs/2301.10051

[26] Wang C Y, Yeh I H, Liao H Y M. YOLOv9: learning what you want to learn using programmable gradient information[C], 1-21(2024). https://doi.org/10.1007/978-3-031-72751-1_1.

[27] Gevorgyan Z. SIoU loss: more powerful learning for bounding box regression[Z](2022). https://arxiv.org/abs/2205.12740

[28] Wang A, Chen H, Liu L H et al. YOLOv10: real-time end-to-end object detection[Z](2024). https://arxiv.org/abs/2405.14458