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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: 25 September 2024 | Viewed by 9865

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Special Issue Editor

Prof. Dr. Wai Lun Lo

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Guest Editor

Department of Computer Science, Chu Hai College of Higher Education, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, Hong Kong 999077, China
Interests: adaptive control; fuzzy control; applications of computer vision; intelligent control; application of artificial intelligence to the design of power electronic systems
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Special Issue Information

Dear Colleagues,

Intelligent control and computer vision are popular research areas in the field of science and engineering. Intelligent control is a control technique that uses various artificial intelligence computing approaches to solve complex control problems. These artificial intelligence computing approaches include neural networks, machine learning control, reinforcement learning, fuzzy and neuro-fuzzy systems, and the evolutionary computation method. All these computing approaches can help us to final an optimal control solution for a linear or nonlinear system with changing parameters and outside disturbances. With the rapid development of sensor technology, control systems today can provide optimal performance for changing parameters and environments.

Computer vision is also a popular interdisciplinary research area in computer science for solving the problems of high-level knowledge extraction from digital images and videos. With the rapid development of sensor technology, computer vision can also allow machines to perform automated tasks that the human visual system can do. Computer vision involves methods for acquiring, processing, analyzing, and understanding digital images, and the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. In this process, we need to transform the context of the digital images to meaningful knowledge or appropriate response actions. Computer vision also involves the utilization of image processing and recognition, artificial intelligence, and machine learning theory.

This Special Issue calls for high-quality, up-to-date research related to innovative sensor technologies for intelligent control and computer vision. In particular, the Special Issue is going to be a sharing platform for the most recent achievements and developments in sensor technology for intelligent control and computer vision. All the submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this Special Issue.

We would like to invite authors to submit articles related to the utilization of new sensor technology for advanced intelligent control systems and computer vision systems to this Special Issue.

Prof. Dr. Wai Lun Lo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

Advanced sensor technology for control/computer vision
Computer vision
Image processing
Pattern recognition
Knowledge extraction from computer vision
Applications of advanced computer vision systems
Intelligent control systems
Applications of intelligent control systems
Neural networks for control/computer vision
Fuzzy and neuro-fuzzy systems for control
Evolutionary computation methods for control/computer vision
Machine learning for control/computer vision
Reinforcement learning for control/computer vision

Published Papers (5 papers)

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Research

18 pages, 3710 KiB

Open AccessArticle

PV Panel Model Parameter Estimation by Using Neural Network

by Wai Lun Lo, Henry Shu Hung Chung, Richard Tai Chiu Hsung, Hong Fu and Tak Wai Shen

Sensors 2023, 23(7), 3657; https://doi.org/10.3390/s23073657 - 31 Mar 2023

Cited by 1 | Viewed by 1452

Abstract

Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions. Full article

(This article belongs to the Special Issue Sensor Technology for Intelligent Control and Computer Visions)

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13 pages, 23788 KiB

Open AccessArticle

Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation

by Yin Jia, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng and Prabakaran Veerajagadheswar

Sensors 2023, 23(4), 2337; https://doi.org/10.3390/s23042337 - 20 Feb 2023

Cited by 1 | Viewed by 1745

Abstract

Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot’s position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%. Full article

(This article belongs to the Special Issue Sensor Technology for Intelligent Control and Computer Visions)

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19 pages, 11577 KiB

Open AccessArticle

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

by Michał Bembenek, Teodor Mandziy, Iryna Ivasenko, Olena Berehulyak, Roman Vorobel, Zvenomyra Slobodyan and Liubomyr Ropyak

Sensors 2022, 22(19), 7600; https://doi.org/10.3390/s22197600 - 07 Oct 2022

Cited by 11 | Viewed by 1623

Abstract

This paper describes the combined detection of coating and rust damages on painted metal structures through the multiclass image segmentation technique. Our prior works were focused solely on the localization of rust damages and rust segmentation under different ambient conditions (different lighting conditions, presence of shadows, low background/object color contrast). This paper method proposes three types of damages: coating crack, coating flaking, and rust damage. Background, paint flaking, and rust damage are objects that can be separated in RGB color-space alone. For their preliminary classification SVM is used. As for paint cracks, color features are insufficient for separating it from other defect types as they overlap with the other three classes in RGB color space. For preliminary paint crack segmentation we use the valley detection approach, which analyses the shape of defects. A multiclass level-set approach with a developed penalty term is used as a framework for the advanced final damage segmentation stage. Model training and accuracy assessment are fulfilled on the created dataset, which contains input images of corresponding defects with respective ground truth data provided by the expert. A quantitative analysis of the accuracy of the proposed approach is provided. The efficiency of the approach is demonstrated on authentic images of coated surfaces. Full article

(This article belongs to the Special Issue Sensor Technology for Intelligent Control and Computer Visions)

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19 pages, 5106 KiB

Open AccessArticle

Method for Determining Treated Metal Surface Quality Using Computer Vision Technology

by Anas M. Al-Oraiqat, Tetiana Smirnova, Oleksandr Drieiev, Oleksii Smirnov, Liudmyla Polishchuk, Sheroz Khan, Yassin M. Y. Hasan, Aladdein M. Amro and Hazim S. AlRawashdeh

Sensors 2022, 22(16), 6223; https://doi.org/10.3390/s22166223 - 19 Aug 2022

Cited by 3 | Viewed by 1719

Abstract

Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements. Full article

(This article belongs to the Special Issue Sensor Technology for Intelligent Control and Computer Visions)

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11 pages, 4433 KiB

Open AccessArticle

Recording Natural Head Position Using Cone Beam Computerized Tomography

by Tai-Chiu Hsung, Wai-Kan Yeung, Wing-Shan Choi, Wai-Kuen Luk, Yi-Yung Cheng and Yu-Hang Lam

Sensors 2021, 21(24), 8189; https://doi.org/10.3390/s21248189 - 08 Dec 2021

Cited by 1 | Viewed by 1957

Abstract

The purpose of this study was to develop a technique to record the natural head position (NHP) of a subject using the scout images of cone beam computerized tomography (CBCT) scans. The first step was to align a hanging mirror with the vertical (XY) plane of the CBCT field-of-view (FOV) volume. Then, two scout CBCT images, at frontal and at sagittal planes, were taken when the subject exhibited a NHP. A normal CBCT scan on the subject was then taken separately. These scout images were used to correct the orientation of the normal CBCT scan. A phantom head was used for validation and performance analysis of the proposed method. It was found that the orientation detection error was within 0.88°. This enables easy and economic NHP recording for CBCT without additional hardware. Full article

(This article belongs to the Special Issue Sensor Technology for Intelligent Control and Computer Visions)

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