Abstract
In this paper, we present rotational region-based fully convolutional networks (RR-FCN) for object detection. In contrast to previous detectors that do not consider rotation, our region-based detector incorporates rotational invariance into networks efficiently and generate more appropriate features according to the rotation angle. Specifically, we propose component-sensitive feature maps, rotational RoI pooling and interceptive back propagation which make RR-FCN learn rotation situations without extra supervision information. Using the 101-layer ResNet model, our method achieves state-of-the-art detection accuracy on PASCAL VOC 2007 and 2012. Moreover, since the feature maps in our network are component-sensitive, RR-FCN can find out objects with various postures, even those appear rarely in the training set. So our RR-FCN has better performance in the real world.
This work is supported by the Natural Science Foundation of China (U1435220) (61503365).
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Zhang, D., Zhang, H., Li, H., Hu, X. (2018). RR-FCN: Rotational Region-Based Fully Convolutional Networks for Object Detection. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_5
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