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Fig. 2 Initial stage of RCC-Net.
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Figure 2 represents the initial stage of the RCC-Net.
Using these settings, the first stage of the RCC-Net is expected to reduce the dimensionality of the input images while extracts the relevant features for the next stages.
Fig. 1 Architecture of RCC-Net.
Nevertheless, the concatenated version of RCC-Net has advantages over the summing one.
1 The progress of RCC-Net performance on the embedded system can be seen at http://te.ugm.ac.id/~igi/?page_id=826.
One interesting result is the pedestrian segmentation of the summing version of the RCC-Net achieves the highest accuracy (70.6%).
As summary of the proposed network, Table 1 exhibits the configuration of the RCC-Net, with 3-channel input images and 11 classes of the road scenes.
Figure 4 depicts some examples of the RCC-Net prediction output on the test set of the CamVid dataset.
Figure 1 expresses the full architecture of the RCC-Net.
Both summing and concatenated convolutional layers of the RCC-Net surpass the other methods.
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