Fig. 2. Overview of FCN.

In our FCN training process, several upsampling layers were added, which enabled convolutional network to produce output layers with image resolution restored to original dimensions. FCN-32 s up-samples stride 32 predictions back to pixels. FCN-16 s combines predictions from both final layer and pooling 4 layers, allowing net to predict finer details while retaining high-level semantic information. FCN-8 s, FCN-4 s, and FCN-2 s receive additional predictions from pooling 3, pooling 2, and pooling 1, respectively, and thereby provide further precision. Conv = convolutional layer, FCN = fully convolutional network