Citation: | SHEN Lingyun, LANG Baihe, SONG Zhengxun, WEN Zhitao. Remote Sensing Image Target Detection Method Based on CSE-YOLOv5[J]. Infrared Technology , 2023, 45(11): 1187-1197. |
[1] |
WANG K, LI Z, SU A, et al. Oriented object detection in optical remote sensing images: a survey[J/OL]. Computer Science, 2023, https://arxiv.org/abs/2302.10473.
|
[2] |
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
|
[3] |
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. DOI: 10.1145/3065386
|
[4] |
Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision (ICCV), 2015: 1440-1448.
|
[5] |
LIU Wei, Dragomir Anguelov, Dumitru Erhan, et al. SSD: single shot multibox detector[J/OL]. Computer Science, 2015, https://arxiv.org/abs/1512.02325.
|
[6] |
LIN Tsungyi, Goyal Priya, Girshick Ross, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327. DOI: 10.1109/TPAMI.2018.2858826
|
[7] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 779-788.
|
[8] |
ZHANG S, WEN L, BIAN X, et al. Single-shot refinement neural network for object detection[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4203-4212, Doi: 10.1109/CVPR.2018.00442.
|
[9] |
CHEN H B, JIANG S, HE G, et al. TEANS: A target enhancement and attenuated no maximum suppression object detector for remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(4): 632-636.
|
[10] |
HOU L, LU K, XUE J, et al. Cascade detector with feature fusion for arbitrary-oriented objects in remote sensing images[C]//IEEE International Conference on Multimedia and Expo (ICME), 2020: 1-6. Doi: 10.1109/ICME46284.2020.9102807.
|
[11] |
LU X, JI J, XING Z, et al. Attention and feature fusion SSD for remote sensing object detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-9.
|
[12] |
LI Q, MOU L, LIU Q, et al. HSF-Net: multiscale deep feature embedding for ship detection in optical remote sensing imagery[J/OL]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 7147-7161.
|
[13] |
DONG R C, XU D Z, ZHAO J, et al. Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8534-8545. DOI: 10.1109/TGRS.2019.2921396
|
[14] |
LI C, LUO B, HONG H, et al. Object detection based on global-local saliency constraint in aerial images[J/OL]. Remote Sensing, 2020, 12(9): 1435, https://doi.org/10.3390/rs12091435.
|
[15] |
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 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021: 2778-2788.
|
[16] |
YANG X, YAN J, FENG Z, et al. R3Det: Refined single-stage detector with feature refinement for rotating object[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022: 3163-3171.
|
[17] |
QING Y, LIU W, FENG L, et al. Improved YOLO network for free-angle remote sensing target detection[J]. Remote Sensing, 2021, 13(11): 2171. DOI: 10.3390/rs13112171
|
[18] |
LONG Y, GONG Y, XIAO Z, et al. Accurate object localization in remote sensing images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5): 2486-2498. DOI: 10.1109/TGRS.2016.2645610
|
[19] |
XU D, WU Y. FE-YOLO: A feature enhancement network for remote sensing target detection[J]. Remote Sensing, 2021, 13(7): 1311. DOI: 10.3390/rs13071311
|
[20] |
CHEN L, SHI W, DENG D. Improved YOLOv3 based on attention mechanism for fast and accurate ship detection in optical remote sensing images[J]. Remote Sensing, 2021, 13(4): 660. DOI: 10.3390/rs13040660
|
[21] |
XU D, WU Y. Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection[J]. Sensors, 2020, 20(15): 4276. DOI: 10.3390/s20154276
|
[22] |
赵玉卿, 贾金露, 公维军, 等. 基于pro-YOLOv4的多尺度航拍图像目标检测算法[J]. 计算机应用研究, 2021, 38(11): 3466-3471. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202111048.htm
ZHAO Y Q, JIA J L, GONG W J, et al. Multi-scale aerial image target detection algorithm based on pro-YOLOv4[J]. Application Research of Computers, 2021, 38(11): 3466-3471. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202111048.htm
|
[23] |
Gevorgyan Z. SIoU Loss: more powerful learning for bounding box regression[J/OL]. Computer Science, 2022, https://arxiv.org/abs/2205.12740.
|
[24] |
王建军, 魏江, 梅少辉, 等. 面向遥感图像小目标检测的改进YOLOv3算法[J]. 计算机工程与应用, 2021, 57(20): 133-141. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202120016.htm
WANG J J, WEI J, MEI S H, et al. Improved Yolov3 for small object detection in remote sensing image[J]. Computer Engineering and Applications, 2021, 57(20): 133-141. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202120016.htm
|
[25] |
XU Z, XU X, WANG L, et al. Deformable ConvNet with aspect ratio constrained NMS for object detection in remote sensing imagery[J]. Remote Sensing, 2017, 9(12): 1312. DOI: 10.3390/rs9121312
|
[26] |
Sanghyun Woo, Jongchan Park, Joon-Young Lee, et al. CBAM: convolutional block attention module[J/OL]. Computer Science, 2018, https://arxiv.org/abs/1807.06521.
|
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