[1] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014:580-587.
|
[2] |
LI C Z, XU C Y, CUI Z, et al. Feature-attentioned object detection in remote sensing imagery[C]// 2019 IEEE International Conference on Image Processing. 2019:3886-3890.
|
[3] |
GIRSHICK R. Fast R-CNN[C]// Proceedings of 2015 IEEE International Conference on Computer Vision. 2015:1440-1448.
|
[4] |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]// European Conference on Computer Vision. 2016:21-37.
|
[5] |
SHRIVASTAVA A, SUKTHANKAR R, MALIK J, et al. Beyond skip connections: Top-down modulation for object detection[J]. Computer Vision and Pattern Recognition, 2016:arXiv:1612.06851.
|
[6] |
DAI J F, LI Y, HE K M, et al. R-FCN: Object detection via region-based fully convolutional networks[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016:379-387.
|
[7] |
KONG T, SUN F C, YAO A B, et al. RON: Reverse connection with objectness prior networks for object detection[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2017:5936-5944.
|
[8] |
XIA G S, BAI X, DING J, et al. DOTA: A large-scale dataset for object detection in aerial images[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:3974-3983.
|
[9] |
CHENG G, ZHOU P C, HAN J W. Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(12):7405-7415.
|
[10] |
SUN P, CHEN G, LUKE G, et al. Salience Biased loss for object detection in aerial images[J]. Computer Vision and Pattern Recognition, 2018:arXiv:1810.08103.
|
[11] |
XU Z 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.
|
[12] |
YAN J Q, WANG H Q, YAN M L, et al. IoU-adaptive deformable R-CNN: Make full use of IoU for multi-class object detection in remote sensing imagery[J]. Remote Sensing, 2019,11(3):286.
|
[13] |
FU K, CHEN Z, ZHANG Y, et al. Enhanced feature representation in detection for optical remote sensing images[J]. Remote Sensing, 2019,11(18):2095.
|
[14] |
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, 2017(6):1137-1149.
|
[15] |
LIN T Y, DOLLR P, GIRSHICK R, et al. Feature pyramid networks for object detection[J]. Computer Vision and Pattern Recognition, 2017:arXiv:1612.03144.
|
[16] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
|
[17] |
CAI Z W, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection[C]// Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition. 2018:6154-6162.
|
[18] |
HAN X B, ZHONG Y F, ZHANG L P. An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery[J]. Remote Sensing, 2017,9(7):666.
|
[19] |
DAI J F, QI H Z, XIONG Y W, et al. Deformable convolutional networks[J]. Computer Vision and Pattern Recognition, 2017:arXiv:1703.06211.
|
[20] |
REN Y, ZHU C R, XIAO S P. Deformable faster R-CNN with aggregating multi-layer features for partially occluded object detection in optical remote sensing images[J]. Remote Sensing, 2018,10(9):1470.
|
[21] |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:3431-3440.
|
[22] |
WANG J F, YUAN Y, YU G. Face attention network: An effective face detector for the occluded faces[J]. Computer Vision and Pattern Recognition, 2017:arXiv:1711.07246.
|
[23] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:7132-7141.
|
[24] |
ZHANG S F, WEN L Y, BIAN X, et al. Single-shot refinement neural network for object detection[J]. Computer Vision and Pattern Recognition, 2018:arXiv:1711.06897.
|
[25] |
GUO W, YANG W, ZHANG H J, et al. Geospatial ob- ject detection in high resolution satellite images based on multi-scale convolutional neural network[J]. Remote Sensing, 2018,10(1):131.
|
[26] |
ABADI M, BARHAM P, CHEN J, et al. Tensorflow: A system for large-scale machine learning[C]// Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation. 2016:265-283.
|
[27] |
AZIMI S M, VIG E, BAHMANYAR R, et al. Towards multi-class object detection in unconstrained remote sensing imagery[J]. Computer Vision and Pattern Recognition, 2018:arXiv:1807.02700.
|
[28] |
DING J, XUE N, LONG Y, et al. Learning ROI transformer for detecting oriented objects in aerial images[J]. Computer Vision and Pattern Recognition, 2018:arXiv:1812.00155.
|
[29] |
SHEN Z Q, LIU Z, LI J G, et al. DSOD: Learning deeply supervised object detectors from scratch[C]// 2017 IEEE International Conference on Computer Vision. 2017:1937-1945.
|
[30] |
CHEN S Q, ZHAN R H, ZHANG J. Geospatial object detection in remote sensing imagery based on multiscale single-shot detector with activated semantics[J]. Remote Sensing, 2018,10(6):820-840.
|