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Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection
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DOI：10.1109/LSP.2020.3025128.png)
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Abstract
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Due to the deteriorated quality of feature in the propagation process of the neural network, it may be hard for traditional detector to identify a small object by just utilizing information within one region proposal. To overcome the limitation of the traditional object detector, we proposed a graph based relation-aware network, to capture the relation information from labels, and images. The semantic relation network is proposed to mine the global semantic relation in labels, and the spatial relation network is proposed to capture the local spatial relation in images. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the relation-aware network explores more advanced feature information. Experiments on the PASCAL VOC, and MS COCO datasets demonstrate that key relation information significantly improve the performance of object detection with better ability to detect small objects, and reasonable bounding box. The results on COCO dataset demonstrate our method can detect objects robustly, increasing the detection performance of small objects from average precision, and average recall by 31.8%, and 32.3% respectively in performance relative to Faster R-CNN.
Keywords:
Object detection
relation information
semantic relation
spatial relation
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