1 |
ORFANUS D, DE FREITAS E P, ELIASSEN F. Self-organization as a supporting paradigm for military UAV relay networks. IEEE Communications Letters, 2016, 20(4): 804- 807.
doi: 10.1109/LCOMM.2016.2524405
|
2 |
LIBRÁN-EMBID F, KLAUS F, TSCHARNTKE T, et al. Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes-a systematic review. Science of the Total Environment, 2020, 732, 139204.
doi: 10.1016/j.scitotenv.2020.139204
|
3 |
ZHANG K, MING D P, DU S G, et al. Distance weight-graph attention model-based high-resolution remote sensing urban functional zone identification. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 1- 18.
|
4 |
SHEFFIELD J, WOOD E F, PAN M, et al. Satellite remote sensing for water resources management: potential for supporting sustainable development in data-poor regions. Water Resources Research, 2018, 54(12): 9724- 9758.
doi: 10.1029/2017WR022437
|
5 |
ZHANG W, CONG M Y, WANG L P. Algorithms for optical weak small targets detection and tracking: review[C]//Proceedings of International Conference on Neural Networks and Signal Processing. Washington D. C., USA: IEEE Press, 2004: 643-647.
|
6 |
闫钧华, 张琨, 施天俊, 等. 融合多层级特征的遥感图像地面弱小目标检测. 仪器仪表学报, 2022, 43(3): 221- 229.
URL
|
|
YAN J H, ZHANG K, SHI T J, et al. Multi-level feature fusion based dim small ground target detection in remote sensing images. Chinese Journal of Scientific Instrument, 2022, 43(3): 221- 229.
URL
|
7 |
HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2021: 13708-13717.
|
8 |
陈欣, 万敏杰, 马超, 等. 采用多尺度特征融合SSD的遥感图像小目标检测. 光学精密工程, 2021, 29(11): 2672- 2682.
doi: 10.37188/OPE.20212911.2672
|
|
CHEN X, WAN M J, MA C, et al. Recognition of small targets in remote sensing image using multi-scale feature fusion-based shot multi-box detector. Optics and Precision Engineering, 2021, 29(11): 2672- 2682.
doi: 10.37188/OPE.20212911.2672
|
9 |
谢星星, 程塨, 姚艳清, 等. 动态特征融合的遥感图像目标检测. 计算机学报, 2022, 45(4): 735- 747.
URL
|
|
XIE X X, CHENG G, YAO Y Q, et al. Dynamic feature fusion for object detection in remote sensing images. Chinese Journal of Computers, 2022, 45(4): 735- 747.
URL
|
10 |
REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137- 1149.
doi: 10.1109/TPAMI.2016.2577031
|
11 |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2017: 936-944.
|
12 |
SUN P, PIAO J C, CUI X. Object detection in urban aerial image based on advanced YOLOv3 algorithm[C]//Proceedings of the 5th International Conference on Mechanical, Control and Computer Engineering. Washington D. C., USA: IEEE Press, 2021: 2191-2196.
|
13 |
王道累, 杜文斌, 刘易腾, 等. 基于密集连接与特征增强的遥感图像检测. 计算机工程, 2022, 48(6): 251-256, 262.
URL
|
|
WANG D L, DU W B, LIU Y T, et al. Remote sensing images detection based on dense connection and feature enhancement. Computer Engineering, 2022, 48(6): 251-256, 262.
URL
|
14 |
|
15 |
赫晓慧, 宋定君, 李盼乐, 等. 融合多尺度特征的遥感影像道路提取方法. 计算机工程, 2022, 48(8): 196- 205.
URL
|
|
HE X H, SONG D J, LI P L, et al. Remote sensing image road extraction method combined with multi-scale features. Computer Engineering, 2022, 48(8): 196- 205.
URL
|
16 |
CHEN L C, ZHU Y K, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[EB/OL]. [2022-09-05]. https://arxiv.org/pdf/1802.02611.pdf.
|
17 |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2017: 5998-6010.
|
18 |
ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2020: 12993-13000.
|
19 |
ZHANG Y F, REN W Q, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing, 2022, 506, 146- 157.
doi: 10.1016/j.neucom.2022.07.042
|
20 |
|
21 |
|
22 |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2018: 7132-7141.
|
23 |
LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2018: 8759-8768.
|
24 |
SRINIVAS A, LIN T Y, PARMAR N, et al. Bottleneck transformers for visual recognition[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2021: 16514-16524.
|
25 |
XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2018: 3974-3983.
|
26 |
|
27 |
PADILLA R, NETTO S L, DA SILVA E A B. A survey on performance metrics for object-detection algorithms[C]//Proceedings of International Conference on Systems, Signals and Image Processing. Washington D. C., USA: IEEE Press, 2020: 237-242.
|
28 |
SANCHEZ S A, ROMERO H J, MORALES A D. A review: comparison of performance metrics of pretrained models for object detection using the TensorFlow framework. IOP Conference Series: Materials Science and Engineering, 2020, 844, 1- 10.
|
29 |
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//Proceedings of International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2017: 618-626.
|
30 |
TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2020: 10778-10787.
|
31 |
|
32 |
ZHU X K, LYU S C, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of IEEE/CVF International Conference on Computer Vision Workshops. Washington D. C., USA: IEEE Press, 2021: 2778-2788.
|
33 |
WANG C Y, BOCHKOVSKIY A, LIAO H Y M, et al. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2022: 1-10.
|