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Multi-scale Feature Fusion Single Shot Object Detector Based on DenseNet

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Intelligent Robotics and Applications (ICIRA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11744))

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Abstract

SSD (Single Shot Multibox Detector) is one of advanced object detection methods and apparently can detect objects with high accuracy and fast speed. However, detecting small objects accurately remains a problem full of challenges for SSD. To handle this troublesome problem, our paper introduce a multi-scale feature fusion single shot object detector based on DenseNet (MFSOD), which combine the dense convolutional network (DenseNet) with SSD framework. Firstly, we add additional convolutional layers after backbone network to realize multi-scale feature detection. In addition, the feature fusion module is designed to fuse the multi-scale features from different layers, introducing the contextual information in object detection. Finally, we evaluate the proposed method on PASCAL VOC2007 and MS COCO benchmark datasets. The results indicate that our proposed method achieves 78.9% mAP on PASCAL VOC2007 test and 27.1% mAP on MS COCO test-dev2015 at the speed of 23 FPS. MFSOD algorithm outperforms the conventional SSD in aspects of accuracy, especially for small objects, and satisfies the demand of real-time application.

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Acknowledgement

This work reported here was supported by the National Natural Science Foundation of China (Grant No. 51375209), 111 Project (Grant No. B18027), the Six Talent Peaks Project in Jiangsu Province (Grant No. ZBZZ-012), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. SJCX18-0630, KYCX18-1846). Finally, the authors would like to thanks for the support of PASCAL VOC datasets.

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Authors and Affiliations

  1. School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Minghao Zhai, Junchen Liu, Wei Zhang, Chen Liu & Yi Cao

  2. Suzhou Vocational Institute of Industrial Technology, Suzhou, 215104, Jiangsu, China

    Wei Li

  3. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi, 214122, Jiangsu, China

    Minghao Zhai, Junchen Liu, Wei Zhang, Chen Liu & Yi Cao

Authors
  1. Minghao Zhai
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  2. Junchen Liu
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  3. Wei Zhang
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  4. Chen Liu
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  5. Wei Li
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  6. Yi Cao
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Corresponding author

Correspondence to Yi Cao .

Editor information

Editors and Affiliations

  1. Shenyang Institute of Automation, Shenyang, China

    Haibin Yu

  2. Shenyang Institute of Automation, Shenyang, China

    Jinguo Liu

  3. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  4. University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  5. Shenyang Institute of Automation, Shenyang, China

    Yuwang Liu

  6. University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Zhai, M., Liu, J., Zhang, W., Liu, C., Li, W., Cao, Y. (2019). Multi-scale Feature Fusion Single Shot Object Detector Based on DenseNet. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_37

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  • DOI: https://doi.org/10.1007/978-3-030-27541-9_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27540-2

  • Online ISBN: 978-3-030-27541-9

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