scholarly journals Exploring Image Enhancement for Salient Object Detection in Low Light Images

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-19
Author(s):  
Xin Xu ◽  
Shiqin Wang ◽  
Zheng Wang ◽  
Xiaolong Zhang ◽  
Ruimin Hu
Keyword(s):  
Object Detection ◽  
Image Enhancement ◽  
Ground Truth ◽  
Salient Object Detection ◽  
Salient Object ◽  
Low Light ◽  
Low Contrast ◽  
Local Content ◽  
The Difference ◽  
Public Datasets

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

scholarly journals Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Senbo Yan ◽  
Xiaowen Song ◽  
Guocong Liu
Keyword(s):  
Object Detection ◽  
Ground Truth ◽  
Salient Object Detection ◽  
Surface Inspection ◽  
Layer By Layer ◽  
Salient Object ◽  
Feature Maps ◽  
Saliency Maps ◽  
Refinement Mechanism ◽  
Inspection Tasks

In recent years, researches in the field of salient object detection have been widely made in many industrial visual inspection tasks. Automated surface inspection (ASI) can be regarded as one of the most challenging tasks in computer vision because of its high cost of data acquisition, serious imbalance of test samples, and high real-time requirement. Inspired by the requirements of industrial ASI and the methods of salient object detection (SOD), a task mode of defect type classification plus defect area segmentation and a novel deeper and mixed supervision network (DMS) architecture is proposed. The backbone network ResNeXt-101 was pretrained on ImageNet. Firstly, we extract five multiscale feature maps from backbone and concatenate them layer by layer. In addition, to obtain the classification prediction and saliency maps in one stage, the image-level and pixel-level ground truth is trained in a same side output network. Supervision signal is imposed on each side layer to realize deeper and mixed training for the network. Furthermore, the DMS network is equipped with residual refinement mechanism to refine the saliency maps of input images. We evaluate the DMS network on 4 open access ASI datasets and compare it with other 20 methods, which indicates that mixed supervision can significantly improve the accuracy of saliency segmentation. Experiment results show that the proposed method can achieve the state-of-the-art performance.

Salient object detection using a covariance-based CNN model in low-contrast images

2017 ◽  
Vol 29 (8) ◽  
pp. 181-192 ◽  
Author(s):  
Nan Mu ◽  
Xin Xu ◽  
Xiaolong Zhang ◽  
Hong Zhang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Low Contrast

scholarly journals Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

2020 ◽  
Vol 10 (23) ◽  
pp. 8754
Author(s):  
Wajeeha Sultan ◽  
Nadeem Anjum ◽  
Mark Stansfield ◽  
Naeem Ramzan
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Salient Object Detection ◽  
Salient Object ◽  
Global Level ◽  
Low Contrast ◽  
Qualitative And Quantitative ◽  
Exact Boundary ◽  
Quantitative Analyses

Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.

scholarly journals Local and Global Feature Aggregation-Aware Network for Salient Object Detection

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 231
Author(s):  
Zikai Da ◽  
Yu Gao ◽  
Zihan Xue ◽  
Jing Cao ◽  
Peizhen Wang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Learning Technology ◽  
Salient Object ◽  
Global Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Feature Aggregation ◽  
Feature Filtering ◽  
Public Datasets

With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extraction, an attention module for high-quality feature filtering, and an aggregation module with a mechanism for rich salient features to ease the dilution process on the top-down pathway. Experimental results on five public datasets demonstrated that the proposed method improves computational efficiency while maintaining favorable performance.

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