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Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 35880

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Special Issue Editors

Dr. Sandeep Singh Sengar

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Department of Computer Science, Cardiff Metropolitan University, Cardiff, UK
Interests: machine learning; deep learning; computer vision; video processing; medical imaging

Prof. Dr. Yu-Chen Hu

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Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan
Interests: computer vision; digital forensics; information hiding; image and signal processing; data compression; information security; computer network; deep learning; bioinformatics
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Dr. Sachin Sharma

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School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
Interests: cloud computing; OpenFlow; software-defined networking
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Dr. Abdul Wahid

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Département Informatique et Réseaux (INFRES), Telecom Paris, Institute Polytechnique de Paris, Paris, France
Interests: machine/deep learning; artificial intelligence; anomaly detection; fraud analysis; data streams
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Dr. Praveen Kumar Donta

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Distributed Systems Group, Technische Universitat Wien, 1040 Vienna, Austria
Interests: machine learning; artificial intelligence; learning-driven computing continuum and distributed systems
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Special Issue Information

Dear Colleagues,

Computer vision refers to the technologies that enable computers to analyse images. This allows models to interpret images and video datasets and then apply this interpretation for prediction or decision-making tasks. Moreover, significant progress has been made in more traditional application fields such as multimedia, robotics, and medical imaging. Furthermore, new areas of application are emerging, such as augmented reality, autonomous driving, Internet of Things, human–computer interaction, and vision for the blind. Computer vision is increasingly being used in non-traditional fields such as astronomy, nanotechnology, and novel brain imaging methods. In the healthcare industry, computer vision has the potential to add significant value. For example, computers may supplement regular diagnosis, which requires extensive time and knowledge.

This Special Issue aims to present a collection of articles that highlight the progress of machine learning/deep learning, blockchains, sensors, and sensing technologies for various computer vision applications (for example, Industry 5.0, cryptocurrency, intelligent control, renewable energy, domotics, voting mechanisms, healthcare, finance, robotics, real estate processing platforms, autonomous driving, etc.). We aim to cover a wide range of perspectives, from fundamental aspects to diverse applicative configurations, including video dynamics (motion segmentation, action recognition, and object tracking) and 2D image content understanding (classification, detection, and semantic segmentation). Additionally, we are interested in advanced learning techniques for security, privacy, and trustworthiness in computer vision.
We invite reviews and regular research papers covering any of the above-mentioned areas of research or related subjects.

Dr. Sandeep Singh Sengar
Prof. Dr. Yu-Chen Hu
Dr. Sachin Sharma
Dr. Abdul Wahid
Dr. Praveen Kumar Donta
Guest Editors

Manuscript Submission Information

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Keywords

computer vision
time series data analysis
medical image processing
Internet of Medical Things
visual Internet of Things
distributed learning
data analysis
wireless sensor networks
advanced machine learning
blockchain technologies
sensor technologies

Published Papers (10 papers)

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Research

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16 pages, 4302 KiB

Open AccessArticle

Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier

by Yanjuan Wu, Yuzhe He and Yunliang Wang

Sensors 2023, 23(16), 7153; https://doi.org/10.3390/s23167153 - 13 Aug 2023

Cited by 3 | Viewed by 2052

Abstract

The Convolutional Neural Network (CNN) is one of the widely used deep learning models that offers the chance to boost farming productivity through autonomous inference of field conditions. In this paper, CNN is connected to a Support Vector Machine (SVM) to form a new model CNN-SVM; the CNN models chosen are ResNet-50 and VGG16 and the CNN-SVM models formed are ResNet-50-SVM and VGG16-SVM. The method consists of two parts: ResNet-50 and VGG16 for feature extraction and SVM for classification. This paper uses the public multi-class weeds dataset DeepWeeds for training and testing. The proposed ResNet-50-SVM and VGG16-SVM approaches achieved 97.6% and 95.9% recognition accuracies on the DeepWeeds dataset, respectively. The state-of-the-art networks (VGG16, ResNet-50, GoogLeNet, Densenet-121, and PSO-CNN) with the same dataset are accurate at 93.2%, 96.1%, 93.6%, 94.3%, and 96.9%, respectively. In comparison, the accuracy of the proposed methods has been improved by 1.5% and 2.7%, respectively. The proposed ResNet-50-SVM and the VGG16-SVM weed classification approaches are effective and can achieve high recognition accuracy. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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16 pages, 1434 KiB

Open AccessArticle

A Multi-Step Fusion Network for Semantic Segmentation of High-Resolution Aerial Images

by Yirong Yuan, Jianyong Cui, Yawen Liu and Boyang Wu

Sensors 2023, 23(11), 5323; https://doi.org/10.3390/s23115323 - 03 Jun 2023

Cited by 1 | Viewed by 1438

Abstract

The demand for semantic segmentation of ultra-high-resolution remote sensing images is becoming increasingly stronger in various fields, posing a great challenge with concern to the accuracy requirement. Most of the existing methods process ultra-high-resolution images using downsampling or cropping, but using this approach could result in a decline in the accuracy of segmenting data, as it may cause the omission of local details or global contextual information. Some scholars have proposed the two-branch structure, but the noise introduced by the global image will interfere with the result of semantic segmentation and reduce the segmentation accuracy. Therefore, we propose a model that can achieve ultra-high-precision semantic segmentation. The model consists of a local branch, a surrounding branch, and a global branch. To achieve high precision, the model is designed with a two-level fusion mechanism. The high-resolution fine structures are captured through the local and surrounding branches in the low-level fusion process, and the global contextual information is captured from downsampled inputs in the high-level fusion process. We conducted extensive experiments and analyses using the Potsdam and Vaihingen datasets of the ISPRS. The results show that our model has extremely high precision. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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17 pages, 2355 KiB

Open AccessArticle

Fast Semantic Segmentation of Remote Sensing Images Using a Network That Integrates Global and Local Information

by Boyang Wu, Jianyong Cui, Wenkai Cui, Yirong Yuan and Xiancong Ren

Sensors 2023, 23(11), 5310; https://doi.org/10.3390/s23115310 - 03 Jun 2023

Cited by 1 | Viewed by 1213

Abstract

Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet’s mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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14 pages, 1533 KiB

Open AccessArticle

Multi-Branch Attention Learning for Bone Age Assessment with Ambiguous Label

by Bishi He, Zhe Xu, Dong Zhou and Yuanjiao Chen

Sensors 2023, 23(10), 4834; https://doi.org/10.3390/s23104834 - 17 May 2023

Viewed by 1213

Abstract

Bone age assessment (BAA) is a typical clinical technique for diagnosing endocrine and metabolic diseases in children’s development. Existing deep learning-based automatic BAA models are trained on the Radiological Society of North America dataset (RSNA) from Western populations. However, due to the difference in developmental process and BAA standards between Eastern and Western children, these models cannot be applied to bone age prediction in Eastern populations. To address this issue, this paper collects a bone age dataset based on the East Asian populations for model training. Nevertheless, it is laborious and difficult to obtain enough X-ray images with accurate labels. In this paper, we employ ambiguous labels from radiology reports and transform them into Gaussian distribution labels of different amplitudes. Furthermore, we propose multi-branch attention learning with ambiguous labels network (MAAL-Net). MAAL-Net consists of a hand object location module and an attention part extraction module to discover the informative regions of interest (ROIs) based only on image-level labels. Extensive experiments on both the RSNA dataset and the China Bone Age (CNBA) dataset demonstrate that our method achieves competitive results with the state-of-the-arts, and performs on par with experienced physicians in children’s BAA tasks. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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25 pages, 3757 KiB

Open AccessArticle

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

by Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Bharat Bhushan, Bogdan Cristian Florea, Dragos Daniel Taralunga, Fayez Alqahtani and Amr Tolba

Sensors 2023, 23(9), 4201; https://doi.org/10.3390/s23094201 - 22 Apr 2023

Cited by 8 | Viewed by 3068

Abstract

The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP’s game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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17 pages, 5668 KiB

Open AccessArticle

Method of Building Detection in Optical Remote Sensing Images Based on SegFormer

by Meilin Li, Jie Rui, Songkun Yang, Zhi Liu, Liqiu Ren, Li Ma, Qing Li, Xu Su and Xibing Zuo

Sensors 2023, 23(3), 1258; https://doi.org/10.3390/s23031258 - 21 Jan 2023

Cited by 7 | Viewed by 2221

Abstract

An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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19 pages, 2498 KiB

Open AccessArticle

A Machine-Learning–Blockchain-Based Authentication Using Smart Contracts for an IoHT System

by Rajkumar Gaur, Shiva Prakash, Sanjay Kumar, Kumar Abhishek, Mounira Msahli and Abdul Wahid

Sensors 2022, 22(23), 9074; https://doi.org/10.3390/s22239074 - 23 Nov 2022

Cited by 11 | Viewed by 3897

Abstract

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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22 pages, 1066 KiB

Open AccessArticle

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks

by Alaa Saleh, Pallavi Joshi, Rajkumar Singh Rathore and Sandeep Singh Sengar

Sensors 2022, 22(20), 7820; https://doi.org/10.3390/s22207820 - 14 Oct 2022

Cited by 10 | Viewed by 1877

Abstract

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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Review

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29 pages, 2937 KiB

Open AccessReview

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

by Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash and Chaminda Hewage

Sensors 2023, 23(6), 3062; https://doi.org/10.3390/s23063062 - 13 Mar 2023

Cited by 7 | Viewed by 10079

Abstract

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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33 pages, 4927 KiB

Open AccessReview

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

by Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, Edmond Prakash and Chaminda Hewage

Sensors 2023, 23(3), 1279; https://doi.org/10.3390/s23031279 - 22 Jan 2023

Cited by 14 | Viewed by 6484

Abstract

Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework. Full article

(This article belongs to the Special Issue Emerging Machine Learning, Blockchain, Sensor and Sensing Technologies for Computer Vision Applications)

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