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| Feature Pyramid Object Detection Network Based on Function Maintenance |
| XU Chengqi1,2,3, HONG Xuehai1,4 |
1. Strategy Research Center of Information Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190
2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049
3. R&D Center, Institute of Big Data, Cloud Computing Center of the Chinese Academy of Sciences, Shangrao 334000
4. Center of Information Development Strategy and Evaluation, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190 |
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Abstract To solve the problem of feature pyramid network in multi-scale and small object detection, a feature pyramid object detection network based on function maintenance is proposed. Firstly, feature maps are selected in the backbone convolutional architecture to build feature pyramid. For these feature maps of different scales, feature fusion with low loss is carried out from top to bottom using function maintenance fusion module. The strong high-level semantic information is maintained more effectively, and the representation ability for small object of low-level feature maps is greatly enhanced. The detection precision is improved by two-stage features of the proposed network to describe the objects. Finally, context information is fully utilized to further enhance the ability to distinguish multi-scale object. Experiments on PASCAL VOC public dataset show that the detection result of the proposed network is satisfactory. Moreover, the proposed network achieves better results in the case of object occlusion and blur as well.
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Received: 08 January 2020
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Fund:National Key R&D Program of China(No.2016YF
C1401706), Key R&D Program of Jiangxi Province(No.S2019Z
PYFB0553) |
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Corresponding Authors:
HONG Xuehai, Ph.D., researcher. His research interests include high performance computing, big data and cloud computing, artificial intelligence.
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| About author:: XU Chengqi, master student. His research interests include machine learning and computer vision. |
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2020, Vol. 33 
