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2025 , Vol. 30 >Issue 5: 1028 - 1036

DOI: https://doi.org/10.1007/s12204-023-2667-y

Computing & Computer Technologies

CenterRCNN: Two-Stage Anchor-Free Object Detection Using Center Keypoint-Based Region Proposal Network

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  • Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-12-16

  Accepted date: 2023-02-10

  Online published: 2023-11-06

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Abstract

The classic two-stage object detection algorithms such as faster regions with convolutional neural network features (Faster RCNN) suffer from low speed and anchor hyper-parameter sensitive problems caused by dense anchor mechanism in region proposal network (RPN). Recently, the anchor-free method CenterNet shows the effectiveness of perceiving and classifying object by its center. However, the severe coincidence false positive problem between confusing categories caused by the multiple binary classifiers makes it still insufficient in accuracy. We introduce a two-stage network CenterRCNN to take advantage of both and overcome their shortcomings. CenterRPN is proposed as the first stage to give proposals that incorporate the center keypoint idea into RPN to perceive foreground objects, replacing dense anchor-based RPN. Then the proposals are classified by the multi-classifier of RCNN header that focuses more on the difference between confusing categories and only outputs the maximum probability one of them. To sum up, CenterRPN can eliminate the drawbacks of dense anchor based RPN in Faster RCNN, and multi-classifier’s classification ability is better than that of multiple binary classifiers in CenterNet. The experiment demonstrates that CenterRCNN outperforms both basic algorithms in the accuracy, and the speed is improved as compared with Faster RCNN.

Cite this article

LIU Chen, LI Wenfa, XU Yunwen, LI Dewei . CenterRCNN: Two-Stage Anchor-Free Object Detection Using Center Keypoint-Based Region Proposal Network[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 1028 -1036 . DOI: 10.1007/s12204-023-2667-y

References

[1] 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, 39(6): 1137-1149.

[2] KISANTAL M, WOJNA Z, MURAWSKI J, et al. Augmentation for small object detection [DB/OL]. (2019-02-19). https://arxiv.org/abs/1902.07296

[3] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 936-944.

[4] CAI Z W, VASCONCELOS N. Cascade R-CNN: Delving into high quality object detection [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6154-6162.

[5] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2999-3007.

[6] FU C Y, LIU W, RANGA A, et al. DSSD: Deconvolutional single shot detector [DB/OL]. (2017-01-23) https://arxiv.org/abs/1701.06659

[7] TAN M X, PANG R M, LE Q V. EfficientDet: scalable and efficient object detection [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10778-10787.

[8] TAN M X, LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks [C]//International Conference on Machine Learning. Long Beach: IMLS, 2019: 6105-6114.

[9] LIU Z, LIN Y T, CAO Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows [C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 9992-10002.

[10] LAW H, DENG J. CornerNet: detecting objects as paired keypoints[M]//European conference on computer vision. Cham: Springer, 2018: 765-781.

[11] ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points [DB/OL]. (2019-04-16). https://arxiv.org/abs/1904.07850

[12] WANG J Q, CHEN K, YANG S, et al. Region proposal by guided anchoring [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 2960-2969.

[13] EVERINGHAM M, WINN J. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) development kit [EB/OL]. (2012-05-18). http://host.robots.ox.ac.uk/pascal/VOC/voc2012/devkit_doc.pdf

[14] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[M]//European conference on computer vision. Cham: Springer, 2014: 740-755.

[15] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M]//European conference on computer vision. Cham: Springer, 2016: 21-37.

[16] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.

[17] DAI J F, LI Y, HE K M, et al. R-FCN: Object detection via region-based fully convolutional networks [C]// 30th Conference on Neural Information Processing Systems. Barcelona: NIPS, 2016: 379-387.

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