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Object detection based on knowledge graph network

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Abstract

Object detection using convolutional neural networks addresses the recognition problem solely in terms of feature extraction and disregards knowledge and experience to explore higher-level relationships between objects. This paper proposed a knowledge graph network based on a graph convolution network to improve the accuracy of baseline detectors. This network can be integrated into any object detection framework. First, this paper created an experience memory module to store information about categories in the database. When inputting the image to the database, an experience vector for it was obtained. The experience data graph was then constructed by counting the co-occurrences of labels in the dataset. Finally, a graph convolutional neural network was used to extract the relationship between the experience vector and the data graph matrix. This relational pattern can help the baseline detector perform better. Several classical object detectors were then evaluated using the COCO, VOC, and KITTI datasets. The results indicated a significant increase for the baseline detector in mAP using the knowledge graph network.

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Data Availability

The raw/processed data required to reproduce these findings cannot beshared at this time as the data also forms part of an ongoing study.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U1808206, 61972097, and U21A20472, in part by the National Key Research and Development Plan of China under Grant 2021YFB3600503, in part by the Natural Science Foundation of Fujian Province under Grant 2021J01612 and 2020J01494, in part by the Major Science and Technology Project of Fujian Province under Grant 2021HZ022007.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Jianping Li, Guozhen Tan, Huaiwei Si & Yanfei Peng

  2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou University, Fuzhou, 350116, China

    Xiao Ke

  3. Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, 350003, China

    Xiao Ke

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  1. Jianping Li
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Correspondence to Guozhen Tan.

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Li, J., Tan, G., Ke, X. et al. Object detection based on knowledge graph network. Appl Intell 53, 15045–15066 (2023). https://doi.org/10.1007/s10489-022-04116-9

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