MRINet: Multilevel Reverse-Context Interactive-Fusion Network for Detecting Salient Objects in RGB-D Images

Zhou, Wujie; Pan, Sijia; Lei, Jingsheng; Yu, Lu

doi:10.1109/lsp.2021.3092967

Cited by 15 publications

(12 citation statements)

References 36 publications

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“…Including visual spatial executive ability, abstraction ability, orientation, language, memory, naming, attention and other cognitive areas, a total of 30 points, patients with a score <23 points are identified as having cognitive dysfunction [ 9 ]. All the procedures are evaluated by the same physician in the department of Neurology of our hospital [ 10 – 12 ].…”

Section: The Experimental Methodsmentioning

confidence: 99%

The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication

Chen

2022

Journal of Healthcare Engineering

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The paper analyzes the detection of insomnia and carotid artery stenosis by magnetic sensitivity weighted imaging (SWI) based on radio communication and its relationship with cognitive dysfunction. A total of 148 patients with carotid artery stenosis and insomnia admitted to our hospital from January 2020 to June 2021 are selected. According to different detection methods, wireless communication combined with SWI group and conventional group are established respectively, with 74 cases in each group. The conventional group applies CT angiography (CTA) is in line with the intervention mode of patients complaining of sleep at night. In the wireless communication combined with SWI group, the sleep monitoring system of wireless communication combined with SWI detection method is used to observe the imaging detection rate, insomnia detection rate and diagnostic efficiency of the two groups. The differences of PSG index parameters, sleep quality (PSQI) score and cognitive function (MoCA) score of patients with different disease degrees are compared. Pearson correlation coefficient is used to analyze the correlation between PSQI score and MoCA score. SWI sequence scan based on wireless network communication has high efficiency in the diagnosis of carotid artery stenosis, and the sleep status of patients can be better understood by real-time monitoring of patients, which is of great significance for the follow-up development of effective diagnosis and treatment plans and recovery of patients’ cognitive function, and worthy of clinical application.

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Section: The Experimental Methodsmentioning

confidence: 99%

The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication

Chen

2022

Journal of Healthcare Engineering

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“…According to the prediction evaluation, the proposed CNN-RF method is compared to some newly released mechanisms to detect damaged power lines with UAV and the IoT technologies including Convolutional Neural Network (CNN) [20], CNN [37] and Support Vector Machine (CNN-SVM) [23], Focal Phi Loss (FPL) [21,38], and convolutional features and structured constraints (CFSC) [25,39].…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Yuqing

2022

Wireless Communications and Mobile Computing

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Today, energy management based on the digitalization of smart grids by the Internet of Things (IoT) is an emerging paradigm for power line systems. There are several environmental hazards to break down high-voltage power cables such as lightning, severe voltage fluctuations, and incorrect design of electric field distribution. So, identifying faulty high-voltage power lines is one of the most emerging challenges in smart grids to avoid disruption of the power distribution networks. This paper presents a new hybrid Convolutional Neural Network and Relief-F (CNN-RF) algorithm for an energy-aware collaborative learning approach to detect power line systems in smart grids. This hybrid approach ensures the stability and reliability of the defective power line system and improves the energy efficiency of the smart grids. This approach can detect the defective power line recognition using damaged power line images concerning automatic monitoring using Unmanned Aerial Vehicle (UAV) control system and IoT communications. By applying UAV control system and IoT communications on gathering damaged power line images, human faults and environmental hazards for extra data transmission are avoided. Experimental results show that the proposed CNN-RF model represents a high accuracy rate of 92.2% for recognizing damaged power lines. Also, the precision of damaged line detection ratio is higher than other prediction methods by the rate of 92.5%. Finally, the performance of the damaged line prediction approach in the CNN-RF method has a daily minimum cost in the IoT-based smart grids.

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“…2, our proposed architecture has three streams, including one main stream in the middle row (with orange background) and two auxiliary streams in the top and bottom rows (with green background). Inspired by [1], we concatenate paired RGB and thermal images and feed them into main stream to extract combination features. Different from them, since the fusion process requires to fuse features extracted from the same stage, we use three VGG16 [2] networks as the backbone.…”

Section: A Network Architecturementioning

confidence: 99%

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

Tang¹,

Wang²,

Chau³

2022

Preprint

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In this paper, we propose a three-stream adaptive fusion network named TAFNet, which uses paired RGB and thermal images for crowd counting. Specifically, TAFNet is divided into one main stream and two auxiliary streams. We combine a pair of RGB and thermal images to constitute the input of main stream. Two auxiliary streams respectively exploit RGB image and thermal image to extract modality-specific features. Besides, we propose an Information Improvement Module (IIM) to fuse the modality-specific features into the main stream adaptively. Experiment results on RGBT-CC dataset show that our method achieves more than 20% improvement on mean average error and root mean squared error compared with stateof-the-art method. The source code will be publicly available at https://github.com/TANGHAIHAN/TAFNet.

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MRINet: Multilevel Reverse-Context Interactive-Fusion Network for Detecting Salient Objects in RGB-D Images

Cited by 15 publications

References 36 publications

The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication

The Relationship between Insomnia and Internal Carotid Artery Stenosis and Cognitive Dysfunction by Magnetic Sensitivity Weighted Imaging Based on Wireless Network Communication

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

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