[1] |
GIRSHICK R , DONAHUE J , DARRELL T ,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2014: 580-587.
|
[2] |
GIRSHICK R , . Fast R-CNN[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015: 1440-1448.
|
[3] |
REN S Q , HE K M , GIRSHICK R ,et al. Faster R-CNN:towards real-time object detection with region proposal networks[C]// Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence. Piscataway:IEEE Press, 2015: 1137-1149.
|
[4] |
HE K M , GKIOXARI G , DOLLáR P ,, et al . Mask R-CNN[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 2980-2988.
|
[5] |
HUANG L C , YANG Y , DENG Y F ,et al. DenseBox:unifying landmark localization with end to end object detection[C]// Proceedings of the IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015.
|
[6] |
REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 779-788.
|
[7] |
LAW H , DENG J . CornerNet:detecting objects as paired keypoints[J]. International Journal of Computer Vision, 2020,128(3): 642-656.
|
[8] |
LIN T Y , MAIRE M , BELONGIE S ,et al. Microsoft COCO:common objects in context[M]// Computer Vision-ECCV 2014. Cham: Springer International Publishing, 2014: 740-755.
|
[9] |
ZHOU X Y , WANG D Q , KR?HENBüHL P , . Objects as points[J]. arXiv preprint,2019,arXiv:1904.07850.
|
[10] |
ZHOU X Y , ZHUO J C , KRAHENBUHL P . Bottom-up object detection by grouping extreme and center points[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2019.
|
[11] |
MA T S , TIAN W H , KUANG P ,et al. An anchor-free object detector with novel corner matching method[J]. Knowledge-Based Systems, 2021,224: 107083.
|
[12] |
LI Q M , QIANG H , LI J . Conditional random fields as message passing mechanism in anchor-free network for multi-scale pedestrian detection[J]. Information Sciences, 2021,550: 1-12.
|
[13] |
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.
|
[14] |
HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 770-778.
|
[15] |
LIN T Y , DOLLáR P ,, GIRSHICK R , et al . Feature pyramid networks for object detection[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 936-944.
|
[16] |
HU J , SHEN L , SUN G . Squeeze-and-excitation networks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 7132-7141.
|
[17] |
CUBUK E D , ZOPH B , SHLENS J ,et al. RandAugment:practical automated data augmentation with a reduced search space[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE Press, 2020: 3008-3017.
|
[18] |
MA J Q , SHAO W Y , YE H ,et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE Transactions on Multimedia, 2018,20(11): 3111-3122.
|
[19] |
YANG X , LIU Q Q , YAN J C ,et al. R3det:refined single-stage detector with feature refinement for rotating object[J]. arXiv preprint,2019,arXiv:1908.05612.
|
[20] |
JIANG Y Y , ZHU X Y , WANG X B ,et al. R2CNN:rotational region CNN for orientation robust scene text detection[J]. arXiv preprint,2017,arXiv:1706.09579.
|