[1] L Ma, Y T Gou, T Lei et al. Small object detection based on multi-scale feature fusion using remote sensing images. Opto-Electron Eng, 49, 210363(2022).

[2] X Chen, D L Peng, Y Gu. Real-time object detection for UAV images based on improved YOLOv5s. Opto-Electron Eng, 49, 210372(2022).

[3] Y W Wang, Y Guo, X Y Shao. Target detection in remote sensing images based on improved cascade algorithm. Acta Opt Sin, 42, 2428004(2022).

[4] J B Wang, G Cheng, X X Xie et al. Multi-information supervision in optical remote sensing images. Natl Remote Sens Bull, 27, 2726-2735(2023).

[5] D Y Zhang, Z H Zhao, Y G Xie et al. Research on aircraft target detection in remote sensing images based on improved YOLOv8. Autom Appl, 65, 193-195,198(2024).

[6] Y L Zhang, H Y Jin. Detector consistency research on remote sensing object detection. Remote Sens, 15, 4130(2023).

[7] Z Lyu, H F Jin, T Zhen et al. Small object recognition algorithm of grain pests based on SSD feature fusion. IEEE Access, 9, 43202-43213(2021).

[8] T Y Lin, P Goyal, R Girshick et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 42, 318-327(2020).

[9] J Redmon, S Divvala, R Girshick et al. You only look once: unified, real-time object detection, 779-788(2016). https://doi.org/10.1109/CVPR.2016.91

[10] J Redmon, A Farhadi. YOLO9000: better, faster, stronger, 6517-6525(2017). https://doi.org/10.1109/CVPR.2017.690

[11] J Redmon, A Farhadi. YOLOv3: an incremental improvement(2018). https://doi.org/10.48550/arXiv.1804.02767

[12] A Bochkovskiy, C Y Wang, H Y M Liao. YOLOv4: optimal speed and accuracy of object detection(2020). https://doi.org/10.48550/arXiv.2004.10934

[13] Z Ge, S T Liu, F Wang et al. YOLOX: exceeding YOLO series in 2021(2021). https://doi.org/10.48550/arXiv.2107.08430

[14] R Girshick. Fast R-CNN, 1440-1448(2015). https://doi.org/10.1109/ICCV.2015.169

[15] J F Dai, Y Li, K M He et al. R-FCN: object detection via region-based fully convolutional networks, 29(2016).

[16] Y T Li, Q S Fan, H S Huang et al. A modified YOLOv8 detection network for UAV aerial image recognition. Drones, 7, 304(2023).

[17] F Z Zhu, Y Y Wang, J Y Cui et al. Target detection for remote sensing based on the enhanced YOLOv4 with improved BiFPN. Egypt J Remote Sens Space Sci, 26, 351-360(2023).

[18] X X Zhai, Z H Huang, T Li et al. YOLO-Drone: an optimized YOLOv8 network for tiny UAV object detection. Electronics, 12, 3664(2023).

[19] F Y Zhou, H G Deng, Q G Xu et al. CNTR-YOLO: improved YOLOv5 based on ConvNext and transformer for aircraft detection in remote sensing images. Electronics, 12, 2671(2023).

[20] B Y Zhu, Q B Lv, Y B Yang et al. Gradient structure information-guided attention generative adversarial networks for remote sensing image generation. Remote Sens, 15, 2827(2023).

[21] J S Xiao, H W Guo, Y T Yao et al. Multi-scale object detection with the pixel attention mechanism in a complex background. Remote Sens, 14, 3969(2022).

[22] J J Wu, L M Su, Z W Lin et al. Object detection of flexible objects with arbitrary orientation based on rotation-adaptive YOLOv5. Sensors, 23, 4925(2023).

[23] X Yang, J C Yan, W L Liao et al. SCRDet++: detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. IEEE Trans Pattern Anal Mach Intell, 45, 2384-2399(2023).

[24] H Y Zhang, J Liu. Direction estimation of aerial image object based on neural network. Remote Sens, 14, 3523(2022).

[25] H T Yu, Y J Tian, Q X Ye et al. Spatial transform decoupling for oriented object detection, 6782-6790(2024). https://doi.org/10.1609/aaai.v38i7.28502

[26] C Li, A B Yao. KernelWarehouse: towards parameter-efficient dynamic convolution(2023). https://doi.org/10.48550/arXiv.2308.08361

[27] S L Ma, Y Xu. MPDIoU: a loss for efficient and accurate bounding box regression(2023). https://doi.org/10.48550/arXiv.2307.07662

[28] J W Wang, C Xu, W Yang et al. A normalized Gaussian Wasserstein distance for tiny object detection(2021). https://doi.org/10.48550/arXiv.2110.13389

[29] M Sandler, A Howard, M L Zhu et al. MobileNetV2: inverted residuals and linear bottlenecks, 4510-4520(2018).

[30] A V Konstantinov, L V Utkin, A A Lukashin et al. Neural attention forests: transformer-based forest improvement(2023). https://doi.org/10.1007/978-3-031-43789-2_14

[31] M X Tan, Q V Le. EfficientNet: rethinking model scaling for convolutional neural networks, 6105-6114(2019).

[32] H Law, J Deng. CornerNet: detecting objects as paired keypoints, 734-750(2018). https://doi.org/10.1007/978-3-030-01264-9_45

[33] A Z Yu, W W Wei, P Wang et al. Small target detection algorithm for UAV based on patch-wise co-attention. Acta Aeronaut Astronaut Sin, 1-12(2023).

[34] L T Min, Z M Fan, Q Y Lv et al. YOLO-DCTI: small object detection in remote sensing base on contextual transformer enhancement. Remote Sens, 15, 3970(2023).