[1] XIA G S, BAI X, DING J et al. DOTA: a large-scale dataset for object detection in aerial images[C], 3974-3983(2018).

[2] HAN W, CHEN J, WANG L Z et al. Methods for small, weak object detection in optical high-resolution remote sensing images: a survey of advances and challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 9, 8-34(2021).

[3] CHENG G, YUAN X, YAO X W et al. Towards large-scale small object detection: survey and benchmarks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 13467-13488(2023).

[4] LI Andong, LIN Zaiping, AN Wei et al. Infrared small target detection in compressive domain based on self-adaptive parameter configuration[J]. Chinese Journal of Lasers, 42, 1008003(2015).

[5] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[6] ZHANG Xiuzai, SHEN Tao, XU Dai. Remote-sensing image object detection based on improved YOLOv8 algorithm[J]. Laser & Optoelectronics Progress, 61, 1028001(2024).

[7] ZHANG J Q, LEI J, XIE W Y et al. SuperYOLO: super resolution assisted object detection in multimodal remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15(2023).

[8] FANG X L, HU F, YANG M et al. Small object detection in remote sensing images based on super-resolution[J]. Pattern Recognition Letters, 153, 107-112(2022).

[9] KISANTAL M, WOJIA Z, MURAWSKI J et al. Augmentation for small object detection[J/OL]. https://arxiv.org/abs/1902.07296

[10] CHEN C R, ZHANG Y, LV Q X et al. RRNet: a hybrid detector for object detection in drone-captured images[C], 100-108(2019).

[11] WU J X, PAN Z X, LEI B et al. FSANet: feature-and-spatial-aligned network for tiny object detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17(2022).

[12] LIU Nan, MAO Zhaoyong, WANG Yichen et al. Remote sensing images target detection based on adjustable parameter and receptive field[J]. Acta Photonica Sinica, 50, 1128001(2021).

[13] XUE Junda, ZHU Jiajia, ZHANG Jing et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 42, 1210002(2022).

[14] WANG Youwei, GUO Ying, SHAO Xiangying. Target detection in remote sensing images based on improved cascade algorithm[J]. Acta Optica Sinica, 42, 2428004(2022).

[15] FU Hongjian, BAI Hongyang, GUO Hongwei et al. Object detection method of optical remote sensing image with multi-attention mechanism[J]. Acta Photonica Sinica, 51, 1210003(2022).

[16] HU Zhaohua, LI Yuhui. Object detection algorithm in remote sensing images based on improved YOLOX[J]. Laser & Optoelectronics Progress, 61, 1228004(2024).

[17] WANG J W, XU C, YANG W et al. A normalized gaussian wasserstein distance for tiny object detection[J/OL]. https://arxiv.org/abs/2110.13389

[18] FU R H, CHEN C C, YAN S et al. Gaussian similarity-based adaptive dynamic label assignment for tiny object detection[J]. Neurocomputing, 543, 126285(2023).

[19] GLENN J. YOLOv5[EB/OL]. https:// github.com/ultralytics/yolov5

[20] YU X H, GONG Y Q, JIANG N et al. Scale match for tiny person detection[C], 1246-1254(2020).

[21] WANG C Y, LIAO H Y M, WU Y H et al. Cspnet: a new backbone that can enhance learning capability of cnn[C], 1571-1580(2020).

[22] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOv4: scaling cross stage partial network[C], 13024-13033(2021).

[23] 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).

[24] GAO S H, CHENG M M, ZHAO K et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).

[25] YU J H, JIANG Y N, WANG Z Y et al. UnitBox: an advanced object detection network[J/OL]. https://arxiv.org/abs/1608.01471

[26] ZHENG Z H, WANG P, LIU W et al. Distance-IoU loss: faster and better learning for bounding box regression[J/OL]. https://arxiv.org/abs/1911.08287

[27] CHEN K, WANG J Q, PANG J M et al. MMDetection: open MMLab detection toolbox and benchmark[J/OL]. https://arxiv. org/abs/ 1906.07155

[28] LIU Z, LIN Y T, CAO Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

[29] GLENN J. YOLOv8[EB/OL]. https://github.com/ultralytics/ultralytics

[30] CAI Z W, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C], 6154-6162(2018).

[31] 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).

[32] SUN P Z, ZHANG R F, JIANG Y et al. Sparse R-CNN: end-to-end object detection with learnable proposals[C], 14449-14458(2018).

[33] QIAO S Y, CHEN L C, YUILLE A. DetectoRS: detecting objects with recursive feature pyramid and switchable atrous convolution[C], 10208-10219(2021).

[34] HE K M, GKIOXARI G, DOLLAR P et al. Mask R-CNN[C], 2980-2988(2017).

[35] GE Z, LIU S T, WANG F et al. YOLOX: exceeding YOLO series in 2021[J/OL]. https://arxiv.org/abs/2107.08430

[36] LI C Y, LI L L, JIANG H L et al. YOLOv6: a single-stage object detection framework for industrial applications[J/OL]. https://arxiv.org/abs/2209.02976