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Densely convolutional and feature fused object detector

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Abstract

In this paper, we propose a novel deep convolutional network for object detection named densely convolutional and feature fused object detector(DCFF-Net), which is a one-stage object detector from scratch similarly to DSOD. The base network is stacking by several densely convolutional blocks to extract the powerful semantic information, and the feature fusion module is used to obtain the enriching features by fusing the extracted feature maps from different convolutional layers. In the fusion module, the feature maps are concatenated of three adjacent scales, which are from the features extracted by the convolution with big kernels, the features extracted by down-sampling pooling and the features extracted by up-sampling deconvolution. The fused feature pyramid has more representative information and gets better performances when it is fed to the final multibox detectors. On the Pascal VOC 2007/2012 and MS COCO, our network achieves better results than DSOD and several methods with pre-training models. The experimental results show that our proposed network has better detection performance by the aid of the fusion of different layers’ feature maps, especially on small objects and occluded objects.

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Acknowledgment

This work is supported by the Natural Science Foundation of China (Grant 61572214 and U1536203), Independent Innovation Research Fund Sponsored by Huazhong university of science and technology (Project No. 2016YXMS089).

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jingjuan Guo, Caihong Yuan, Ping Feng, Tianjiang Wang & Kui Duan

  2. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, China

    Jingjuan Guo & Zhiqiang Zhao

  3. School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Caihong Yuan

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  1. Jingjuan Guo
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  2. Caihong Yuan
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  3. Zhiqiang Zhao
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  4. Ping Feng
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  5. Tianjiang Wang
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  6. Kui Duan
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Correspondence to Jingjuan Guo or Kui Duan.

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Guo, J., Yuan, C., Zhao, Z. et al. Densely convolutional and feature fused object detector. Multimed Tools Appl 78, 35559–35584 (2019). https://doi.org/10.1007/s11042-019-08119-6

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