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Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 17854

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Prof. Dr. Marcin Woźniak

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Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Interests: computational intellgence; neural networks; image processing; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent years brought vast development in various methodologies for object detection, feature extraction and recognition both in theory and practice. When processing images, video or other multimedia, we need efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screenings where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques that help us to recognize some special features. In the context of this innovative research on computational intelligence, it is my pleasure to invite you to contribute to this Special Issue of Sensors, which presents an excellent opportunity for the dissemination of your recent results and cooperation for further innovations.

Topics of interest:

Bio-inspired methods, deep learning, convolutional neural networks, hybrid architectures, etc.;
Time series, fractional-order controllers, gradient field methods, surface reconstruction, and other mathematical models for intelligent feature detection, extraction; and recognition;
Embedded intelligent computer vision algorithms;
Human, nature, technology, and various object activity recognition models;
Hyper-parameter learning and tuning, automatic calibration, hybrid, and surrogate learning for computational intelligence in vision systems;
Intelligent video and image acquisition techniques.

Prof. Dr. Marcin Woźniak
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

20 pages, 10041 KiB

Open AccessArticle

Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion

by Maziar Jamshidi, Mamdouh El-Badry and Navid Nourian

Sensors 2023, 23(1), 504; https://doi.org/10.3390/s23010504 - 02 Jan 2023

Cited by 5 | Viewed by 2124

Abstract

A key element in an automated visual inspection system for concrete structures is identifying the geometric properties of surface defects such as cracks. Fully convolutional neural networks (FCNs) have been demonstrated to be powerful tools for crack segmentation in inspection images. However, the performance of FCNs depends on the size of the dataset that they are trained with. In the absence of large datasets of labeled images for concrete crack segmentation, these networks may lose their excellent prediction accuracy when tested on a new target dataset with different image conditions. In this study, firstly, a Transfer Learning approach is developed to enable the networks better distinguish cracks from background pixels. A synthetic dataset is generated and utilized to fine-tune a U-Net that is pre-trained with a public dataset. In the proposed data synthesis approach, which is based on CutMix data augmentation, the crack images from the public dataset are combined with the background images of a potential target dataset. Secondly, since cracks propagate over time, for sequential images of concrete surfaces, a novel temporal data fusion technique is proposed. In this technique, the network’s predictions from multiple time steps are aggregated to improve the recall of predictions. It is shown that application of the proposed improvements has increased the F1-score and mIoU by 28.4% and 22.2%, respectively, which is a significant enhancement in performance of the segmentation network. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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34 pages, 36813 KiB

Open AccessArticle

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

by Polina Lemenkova, Raphaël De Plaen, Thomas Lecocq and Olivier Debeir

Sensors 2023, 23(1), 56; https://doi.org/10.3390/s23010056 - 21 Dec 2022

Cited by 2 | Viewed by 2244

Abstract

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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25 pages, 3483 KiB

Open AccessEditor’s ChoiceArticle

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

by Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov and Jinsoo Cho

Sensors 2022, 22(23), 9384; https://doi.org/10.3390/s22239384 - 01 Dec 2022

Cited by 29 | Viewed by 5789

Abstract

Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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21 pages, 8677 KiB

Open AccessArticle

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM)

by Ali Rohan

Sensors 2022, 22(23), 9064; https://doi.org/10.3390/s22239064 - 22 Nov 2022

Cited by 6 | Viewed by 1279

Abstract

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or the filters are used to extract features autonomously (in the case of DL) to perform the critical classification task. In particular, in the fault detection and diagnosis of industrial robots where the primary sources of information are electric current, vibration, or acoustic emissions signals that are rich in information in both the temporal and frequency domains, techniques capable of extracting meaningful information from non-stationary frequency-domain signals with the ability to map the signals into their constituent components with compressed information are required. This has the potential to minimise the complexity and size of traditional ML- and DL-based frameworks. The deep scattering spectrum (DSS) is one of the approaches that use the Wavelet Transform (WT) analogy for separating and extracting information embedded in a signal’s various temporal and frequency domains. Therefore, the primary focus of this work is the investigation of the efficacy and applicability of the DSS’s feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots. For this, multiple industrial robots with distinct mechanical faults were studied. Data were collected from these robots under different fault conditions and an approach was developed for classifying the faults using DSS’s low-variance features extracted from input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. The results suggest that, similarly to other ML techniques, the DSS offers significant potential in addressing fault classification challenges, especially for cases where the data are in the form of signals. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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17 pages, 6208 KiB

Open AccessArticle

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments

by Suppat Metarugcheep, Proadpran Punyabukkana, Dittaya Wanvarie, Solaphat Hemrungrojn, Chaipat Chunharas and Ploy N. Pratanwanich

Sensors 2022, 22(15), 5813; https://doi.org/10.3390/s22155813 - 03 Aug 2022

Cited by 1 | Viewed by 2234

Abstract

Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as “F” in English and “ก” /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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21 pages, 4452 KiB

Open AccessArticle

Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds

by Salil Bharany, Kiranbir Kaur, Sumit Badotra, Shalli Rani, Kavita, Marcin Wozniak, Jana Shafi and Muhammad Fazal Ijaz

Sensors 2022, 22(13), 5013; https://doi.org/10.3390/s22135013 - 02 Jul 2022

Cited by 35 | Viewed by 3014

Abstract

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer’s overhead of implementing each service for different clouds to make it portable. Full article

(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)

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