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Object detection based on knowledge graph network
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AI
DOI：10.1007/s10489-022-04116-9.png)
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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.
Keyword:
Knowledge and experience
Higher-level relationships
Knowledge graph network
Graph convolution network
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