Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects

TL;DR

This paper compares Fully Convolutional Networks and Mask R-CNNs for Salient Object Detection, demonstrating Mask R-CNNs' superior performance across multiple datasets and metrics.

Contribution

It provides an extensive comparison between FCNs and Mask R-CNNs in SOD, highlighting the effectiveness of Mask R-CNNs over traditional FCNs.

Findings

Mask R-CNNs outperform FCNs in F-measure by up to 47%.

The study evaluates on eight datasets using four metrics.

Mask R-CNNs show superior segmentation accuracy in challenging scenarios.

Abstract

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.