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A Balanced Feature Fusion SSD for Object Detection

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Abstract

Single shot multibox detector (SSD) takes several feature layers for object detection, but each layer is used independently. This structure may ignore some context information and is not conducive to improving the detection accuracy of small objects. Moreover, the imbalances of samples and multi-tasks during SSD training process can lead to inefficient training and model degradation. In order to improve the performance of SSD, this paper proposes a balanced feature fusion SSD (BFSSD) algorithm. Firstly, a feature fusion module is proposed to fuse and refine different layers of the feature pyramid. Then, a more balanced L1 loss function is proposed to further solve these imbalances. Finally, our model is trained with Pascal VOC2007 and VOC2012 trainval datasets and tested on Pascal VOC2007 test datasets. Simulation results show that, for the input size of 300 × 300, BFSSD exceeds the best results provided by the conventional SSD and other advanced object detection algorithms.

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Notes

  1. YOLO and YOLOv2: http://pjreddie.com/darknet.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61671095). The authors also would like to thank the anonymous reviewers for their valuable comments and suggestions. At the same time, we also want to thank Bo Wang for his help in the process of revising our manuscript.

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hui Zhao, Zhiwei Li, Lufa Fang & Tianqi Zhang

  2. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hui Zhao, Zhiwei Li, Lufa Fang & Tianqi Zhang

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  1. Hui Zhao
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  2. Zhiwei Li
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  3. Lufa Fang
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  4. Tianqi Zhang
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Correspondence to Hui Zhao.

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Zhao, H., Li, Z., Fang, L. et al. A Balanced Feature Fusion SSD for Object Detection. Neural Process Lett 51, 2789–2806 (2020). https://doi.org/10.1007/s11063-020-10228-5

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