Next Article in Journal
Methods for MADM with Picture Fuzzy Muirhead Mean Operators and Their Application for Evaluating the Financial Investment Risk
Previous Article in Journal
Stochastic Bifurcation of a Strongly Non-Linear Vibro-Impact System with Coulomb Friction under Real Noise
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Symmetry 2019, 11(1), 5; https://doi.org/10.3390/sym11010005

High Precision Detection of Salient Objects Based on Deep Convolutional Networks with Proper Combinations of Shallow and Deep Connections

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Received: 30 November 2018 / Revised: 15 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
Full-Text   |   PDF [4831 KB, uploaded 24 December 2018]   |  

Abstract

In this paper, a high precision detection method of salient objects is presented based on deep convolutional networks with proper combinations of shallow and deep connections. In order to achieve better performance in the extraction of deep semantic features of salient objects, based on a symmetric encoder and decoder architecture, an upgrade of backbone networks is carried out with a transferable model on the ImageNet pre-trained ResNet50. Moreover, by introducing shallow and deep connections on multiple side outputs, feature maps generated from various layers of the deep neural network (DNN) model are well fused so as to describe salient objects from local and global aspects comprehensively. Afterwards, based on a holistically nested edge detector (HED) architecture, multiple fused side outputs with various sizes of receptive fields are integrated to form detection results of salient objects accordingly. A series of experiments and assessments on extensive benchmark datasets demonstrate the dominant performance of our DNN model for the detection of salient objects in accuracy, which has outperformed those of other published works. View Full-Text
Keywords: detection of salient objects; deep learning; deep neural networks; semantic segmentation; shallow and deep connections detection of salient objects; deep learning; deep neural networks; semantic segmentation; shallow and deep connections
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Guo, L.; Qin, S. High Precision Detection of Salient Objects Based on Deep Convolutional Networks with Proper Combinations of Shallow and Deep Connections. Symmetry 2019, 11, 5.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top