End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Summary

The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.

Abstract

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.

Introduction

Salient object detection mimics human visual attention, swiftly identifying key image regions while suppressing background information. This technique is widely employed as a pre-processing tool in tasks such as image cropping¹, semantic segmentation², and image editing³. It streamlines tasks like background replacement and foreground extraction, improving editing efficiency and precision. Additionally, it aids in semantic segmentation by enhancing target localization. The potential of salient object detection to enhance computational efficiency and conserve memory underscores its significant rese....

Protocol

1. Experimental setup and procedure

1. Load the pre-trained VGG16 model.
   NOTE: The first step is to load the pre-trained VGG16 model from the Keras library⁶.
   1. To load a pre-trained VGG16 model in Python using popular deep learning libraries like PyTorch (see Table of Materials), follow these general steps:
      1. Import torch. Import torchvision.models as models.
      2. Load the pre-trained V.......

Discussion

The article introduces an end-to-end deep neural network specifically designed for the detection of salient objects in complex environments. The network is composed of two interconnected components: a pixel-level multiscale fully convolutional network (DCL) and a deep encoder-decoder network (DEDN). These components work synergistically, incorporating contextual semantics to generate visual contrasts within multiscale feature maps. Additionally, they leverage both deep and shallow image features to improve the precision .......

Materials

Name	Company	Catalog Number	Comments
Matlab	MathWorks	Matlab R2016a	MATLAB's programming interface provides development tools for improving code quality maintainability and maximizing performance. It provides tools for building applications using custom graphical interfaces. It provides tools for combining MATLAB-based algorithms with external applications and languages
Processor	Intel	11th Gen Intel(R) Core (TM) i5-1135G7 @ 2.40GHz	64-bit Win11 processor
Pycharm	JetBrains	PyCharm 3.0	PyCharm is a Python IDE (Integrated Development Environment) a list of required python: modulesmatplotlib skimage torch os time pydensecrf opencv glob PIL torchvision numpy tkinter
PyTorch	Facebook	PyTorch 1.4	PyTorch is an open source Python machine learning library , based on Torch , used for natural language processing and other applications.PyTorch can be viewed both as the addition of GPU support numpy , but also can be viewed as a powerful deep neural network with automatic derivatives .