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C-FCN: Corners-based fully convolutional network for visual object detection

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Abstract

Object detection has achieved significantly progresses in recent years. Proposal-based methods have become the mainstream object detectors, achieving excellent performance on accurate recognition and localization of objects. However, region proposal generation is still a bottleneck. In this paper, to address the limitations of conventional region proposal network (RPN) that defines dense anchor boxes with different scales and aspect ratios, we propose an anchor-free proposal generator named corner region proposal network (CRPN) which is based on a pair of key-points, including top-left corner and bottom-right corner of an object bounding box. First, we respectively predict the top-left corners and bottom-right corners by two sibling convolutional layers, then we obtain a set of object proposals by grouping strategy and non-maximum suppression algorithm. Finally, we further merge CRPN and fully convolutional network (FCN) into a unified network, achieving an end-to-end object detection. Our method has been evaluated on standard PASCAL VOC and MS COCO datasets using a deep residual network. Experiment results present that the proposed method outperforms previous detectors in the term of precision. Additionally, it runs with a speed of 76 ms per image on a single GPU by using ResNet-50 as the backbone, which is faster than other detectors.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 31671586, 61773360).

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Authors and Affiliations

  1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei, 230031, China

    Lin Jiao, Rujing Wang & Chengjun Xie

  2. University of Science and Technology of China, Hefei, 230026, China

    Lin Jiao

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  1. Lin Jiao
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Correspondence to Chengjun Xie.

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Jiao, L., Wang, R. & Xie, C. C-FCN: Corners-based fully convolutional network for visual object detection. Multimed Tools Appl 79, 28841–28857 (2020). https://doi.org/10.1007/s11042-020-09503-3

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