  In this paper, we propose a broad comparison between Fully Convolutional
Networks (FCNs) and Mask Region-based Convolutional Neural Networks
(Mask-RCNNs) applied in the Salient Object Detection (SOD) context. Studies in
the SOD literature usually explore architectures based in FCNs to detect
salient regions and objects in visual scenes. However, besides the promising
results achieved, FCNs showed issues in some challenging scenarios. Fairly
recently studies in the SOD literature proposed the use of a Mask-RCNN approach
to overcome such issues. However, there is no extensive comparison between the
two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs
over FCN when segmenting salient objects. Aiming to effectively show the
superiority of Mask-RCNNs over FCNs in the SOD context, we compare two
variations of Mask-RCNNs with two variations of FCNs in eight datasets widely
used in the literature and in four metrics. Our findings show that in this
context Mask-RCNNs achieved an improvement on the F-measure up to 47% over
FCNs.
