Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

References in zbMATH (referenced in 72 articles )

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  1. Chen, Peiji; Zhou, Zhangnan; Yu, Haixia; Chen, Kun; Yang, Yun: Computerized-assisted scoliosis diagnosis based on faster R-CNN and ResNet for the classification of spine X-ray images (2022)
  2. Gu, Linyan; Zhang, Wei; Liu, Jia; Cai, Xiao-Chuan: Decomposition and composition of deep convolutional neural networks and training acceleration via sub-network transfer learning (2022)
  3. Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov: MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models (2022) arXiv
  4. Wald, Johanna; Navab, Nassir; Tombari, Federico: Learning 3D semantic scene graphs with instance embeddings (2022)
  5. Zhou, Cheng; Ren, Dacong; Zhang, Xiangyan; Yu, Cungui; Ju, Likai: Human position detection based on depth camera image information in mechanical safety (2022)
  6. Amerini, Irene; Anagnostopoulos, Aris; Maiano, Luca; Celsi, Lorenzo Ricciardi: Deep learning for multimedia forensics (2021)
  7. Chen, Zhe; Zhang, Jing; Tao, Dacheng: Recursive context routing for object detection (2021)
  8. Dai, Ming; Zhou, Zhiheng; Wang, Tianlei; Guo, Yongfan: Image segmentation using level set driven by generalized divergence (2021)
  9. Goncharenko, V. I.; Zheltov, S. Yu.; Knyaz, V. A.; Lebedev, G. N.; Mikhaylin, D. A.; Tsareva, O. Yu.: Intelligent system for planning group actions of unmanned aircraft in observing mobile objects on the ground in the specified area (2021)
  10. Koo, Bongyeong; Choi, Han-Soo; Kang, Myungjoo: Simple feature pyramid network for weakly supervised object localization using multi-scale information (2021)
  11. Kortylewski, Adam; Liu, Qing; Wang, Angtian; Sun, Yihong; Yuille, Alan: Compositional convolutional neural networks: a robust and interpretable model for object recognition under occlusion (2021)
  12. Long, Ziang; Yin, Penghang; Xin, Jack: Learning quantized neural nets by coarse gradient method for nonlinear classification (2021)
  13. Long, Ziang; Yin, Penghang; Xin, Jack: Global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non-separable data (2021)
  14. Luo, Wenhan; Xing, Junliang; Milan, Anton; Zhang, Xiaoqin; Liu, Wei; Kim, Tae-Kyun: Multiple object tracking: a literature review (2021)
  15. Lu, Xi; You, Zejun; Sun, Miaomiao; Wu, Jing; Zhang, Zhihong: Breast cancer mitotic cell detection using cascade convolutional neural network with U-net (2021)
  16. Ma, Cong; Yang, Fan; Li, Yuan; Jia, Huizhu; Xie, Xiaodong; Gao, Wen: Deep human-interaction and association by graph-based learning for multiple object tracking in the wild (2021)
  17. Nie, Yan; Zhang, Taiping; Zhao, Linchang; Ma, Xindi; Tang, Yuanyan; Liu, Xiaoyu: Siamese pyramid residual module with local binary convolution network for single object tracking (2021)
  18. Ostovar, Ahmad; Bensch, Suna; Hellström, Thomas: Natural language guided object retrieval in images (2021)
  19. Peng, Jianzhong; Zhu, Wei; Liang, Qiaokang; Li, Zhengwei; Lu, Maoying; Sun, Wei; Wang, Yaonan: Defect detection in code characters with complex backgrounds based on BBE (2021)
  20. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)

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