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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 20511

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Prof. Dr. Vladimir László Tadić

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Guest Editor

1. Department of Mechanical Engineering, Electrical Engineering and Computer Science, Technical College of Applied Sciences in Zrenjanin, Đorđa Stratimirovića 23, 23000 Zrenjanin, Serbia
2. John von Neumann Faculty of Informatics, Óbuda University, Becsi Str. 96/B, H-1034 Budapest, Hungary
3. Symbolic Methods in Material Analysis and Tomography Research Group, Faculty of Engineering and Information Technology, University of Pecs, Boszorkany Str. 6, H-7624 Pecs, Hungary
4. Institute of Information Technology, University of Dunaujvaros, Tancsics M. Str. 1/A, H-2401 Dunaujvaros, Hungary
Interests: image processing; computer vision; signal processing; electronics; robotics and soft computing methods
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peter Odry

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Guest Editor

1. Institute of Information Technology, University of Dunaujvaros, Tancsics M. Str. 1/A, H-2401 Dunaujvaros, Hungary
2. Symbolic Methods in Material Analysis and Tomography Research Group, Faculty of Engineering and Information Technology, University of Pecs, Boszorkany Str. 6, H-7624 Pecs, Hungary
Interests: robotics; fuzzy control; electrical engineering; optimization methods; electrical impedance tomography; control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For many years, scientists and engineers have tried to make digital image processing as efficient as the human vision system. Recently, artificial intelligence, deep learning and soft computing methods have been involved in the development of various sophisticated image processing algorithms. Further, image processing plays a significant role in intelligent robotics, where the goal is the realization of precise, robust, and intelligent control solutions based on image information. Thus, the use of vision sensors and cameras in robotics inspired the deployment of effective applications in industry, agriculture, biology, medicine, etc.

The aim of this Special Issue is to give researchers the opportunity to provide new tendencies as well as the latest achievements and research directions, and to present their current work on the important problems in image processing, deep learning, soft computing, sensor fusion, robotic vision and applied industrial solutions in robotics.

In this Special Issue, original research articles, short communications, technical reports, perspectives, extended conference papers and reviews are welcome. Research areas may include (but are not limited to) the following:

Two- and three-dimensional image processing;
Image segmentation and texture analysis;
Image filtering, restoration and enhancement;
Biomedical image processing;
Pattern recognition and shape detection;
Deep learning;
Soft computing and fuzzy techniques;
Sensor fusion;
Measurements;
Robot vision;
Intelligent and applied robotics;
Hardware and architectures for image processing and robotics;
Robust identification.

Prof. Dr. Vladimir László Tadić
Prof. Dr. Peter Odry
Guest Editors

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. Electronics 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 2400 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

image processing
medical imaging
artificial intelligence
deep learning
soft computing
fuzzy logic
sensor fusion
measurements
robotic vision
industrial robotics
robust identification

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Published Papers (13 papers)

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21 pages, 3503 KiB

Open AccessArticle

An Effective and Robust Parameter Estimation Method in a Self-Developed, Ultra-Low Frequency Impedance Spectroscopy Technique for Large Impedances

by Bojan Kuljic, Zoltan Vizvari, Nina Gyorfi, Mihaly Klincsik, Zoltan Sari, Florian Kovacs, Katalin Juhos, Tibor Szakall, Akos Odry, Levente Kovacs, Vladimir Tadic, Mirjana Siljegovic, Peter Odry and Istvan Kecskes

Electronics 2024, 13(16), 3300; https://doi.org/10.3390/electronics13163300 - 20 Aug 2024

Viewed by 579

Abstract

Bioimpedance spectrum (BIS) measurements are highly appreciated in in vivo studies. This non-destructive method, supported by simple and efficient instrumentation, is widely used in clinical applications. The multi-frequency approach allows for the efficient extraction of the most information from the measured data. However, low-frequency implementations are still unexploited in the development of the technique. A self-developed BIS measurement technology is considered the pioneering approach for low (<5 kHz) and ultra-low (<100 Hz) frequency range studies. In this paper, the robustness of ultra-low frequency measurements in the prototypes is examined using specially constructed physical models and a dedicated neural network-based software. The physical models were designed to model the dispersion mainly in the ultra-low frequency range. The first set of models was used in the training of the software environment, while the second set allowed a complete verification of the technology. Further, the Hilbert transformation was employed to adjust the imaginary components of complex signals and for phase determination. The findings showed that the prototypes are capable of efficient and robust data acquisition, regardless of the applied frequency range, minimizing the impact of measurement errors. Consequently, in in vivo applications, these prototypes minimize the variance of the measurement results, allowing the resulting BIS data to provide a maximum representation of biological phenomena. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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16 pages, 5937 KiB

Open AccessArticle

Automotive Parts Defect Detection Based on YOLOv7

by Hao Huang and Kai Zhu

Electronics 2024, 13(10), 1817; https://doi.org/10.3390/electronics13101817 - 8 May 2024

Cited by 1 | Viewed by 1948

Abstract

Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection algorithms have been proven to be ineffective, which often leads to missing detection or wrong identification. To address these issues, this paper introduces a defect detection algorithm based on YOLOv7. To enhance the detection of small objects and streamline the model, we incorporate the ECA attention mechanism into the network structure’s backbone. Considering the small sizes of defect targets on automotive parts and the complexity of their backgrounds, we redesign the neck portion of the model. This redesign includes the integration of the BiFPN feature fusion module to enhance feature fusion, with the aim of minimizing missed detections and false alarms. Additionally, we employ the Alpha-IoU loss function in the prediction phase to enhance the model’s accuracy, which is crucial for reducing false detection. The IoU loss function also boosts the model’s efficiency at converging. The evaluation of this model utilized the Northeastern University steel dataset and a proprietary dataset and demonstrated that the average accuracy (mAP) of the MBEA-YOLOv7 detection network was 76.2% and 94.1%, respectively. These figures represent improvements of 5.7% and 4.7% over the original YOLOv7 network. Moreover, the detection speed for individual images ranges between 1–2 ms. This enhancement in detection accuracy for small targets does not compromise detection speed, fulfilling the requirements for real-time, dynamic inspection of defects. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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17 pages, 11165 KiB

Open AccessArticle

A Novel Multi-LiDAR-Based Point Cloud Stitching Method Based on a Constrained Particle Filter

by Gaofan Ji, Yunhan He, Chuanxiang Li, Li Fan, Haibo Wang and Yantong Zhu

Electronics 2024, 13(9), 1777; https://doi.org/10.3390/electronics13091777 - 4 May 2024

Cited by 1 | Viewed by 1122

Abstract

In coal-fired power plants, coal piles serve as the fundamental management units. Acquiring point clouds of coal piles facilitates the convenient measurement of daily coal consumption and combustion efficiency. When using servo motors to drive Light Detection and Ranging (LiDAR) scanning of large-scale coal piles, the motors are subject to rotational errors due to gravitational effects. As a result, the acquired point clouds often contain significant noise. To address this issue, we proposes a Rapid Point Cloud Stitching–Constrained Particle Filter (RPCS-CPF) method. By introducing random noise to simulate servo motor rotational errors, both local and global point clouds are sequentially subjected to RPCS-CPF operations, resulting in smooth and continuous coal pile point clouds. Moreover, this paper presents a coal pile boundary detection method based on gradient region growing clustering. Experimental results demonstrate that our proposed RPCS-CPF method can generate smooth and continuous coal pile point clouds, even in the presence of servo motor rotational errors. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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24 pages, 27317 KiB

Open AccessArticle

An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features

by Yingnan Zhang, Zhizhong Kang and Zhen Cao

Electronics 2024, 13(7), 1262; https://doi.org/10.3390/electronics13071262 - 28 Mar 2024

Cited by 1 | Viewed by 1345

Abstract

In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (

{LC}^{2} R - Net

) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that

{LC}^{2} R - Net

achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of

{LC}^{2} R - Net

in handling the task of lunar complex impact crater image retrieval. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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17 pages, 6023 KiB

Open AccessArticle

Continuous Electrode Models and Application of Exact Schemes in Modeling of Electrical Impedance Measurements

by Zoltan Vizvari, Mihaly Klincsik, Peter Odry, Vladimir Tadic, Nina Gyorfi, Attila Toth and Zoltan Sari

Electronics 2024, 13(1), 66; https://doi.org/10.3390/electronics13010066 - 22 Dec 2023

Viewed by 999

Abstract

The crucial issue in electrical impedance (EI) measurements lies in the galvanic interaction between the electrodes and the investigated material. This paper brings together the basic and applied research experience and combines their results with excellent properties. Consequently, innovative precise methodologies have emerged, enabling the direct modeling of EI measurements, free from the inaccuracies often associated with numerical approaches. As an outcome of the efficiency and robustness of the applied method, the conductivity of the material and the electrodes are represented by a common piecewise function, which is used to solve the differential equation modeling of the EI measurement. Moreover, this allows the possibility for modeling the conductivity of electrodes with continuous functions, providing an important generalization of the Complete Electrode Model (CEM), which has been widely used so far. The effectiveness of the novel approach was showcased through two distinct case studies. In the first case study, potential functions within both the material and the electrodes were computed using the CEM. In the second case study, calculations were performed utilizing the newly introduced continuous electrode model. The simulation results suggest that the new method is a powerful tool for biological research, from in vitro experiments to animal studies and human applications. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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16 pages, 37035 KiB

Open AccessArticle

A Method for Visualization of Images by Photon-Counting Imaging Only Object Locations under Photon-Starved Conditions

by Jin-Ung Ha, Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee

Electronics 2024, 13(1), 38; https://doi.org/10.3390/electronics13010038 - 20 Dec 2023

Cited by 1 | Viewed by 958

Abstract

Recently, many researchers have been studying the visualization of images and the recognition of objects by estimating photons under photon-starved conditions. Conventional photon-counting imaging techniques estimate photons by way of a statistical method using Poisson distribution in all image areas. However, Poisson distribution is temporally and spatially independent, and the reconstructed image has a random noise in the background. Random noise in the background may degrade the quality of the image and make it difficult to accurately recognize objects. Therefore, in this paper, we apply photon-counting imaging technology only to the area where the object is located to eliminate the noise in the background. As a result, it can be seen that the image quality using the proposed method is better than that of the conventional method and the object recognition rate is also higher. Optical experiments were conducted to prove the denoising performance of the proposed method. In addition, we used the structure similarity index measure (SSIM) as a performance metric. To check the recognition rate of the object, we applied the YOLOv5 model. Finally, the proposed method is expected to accelerate the development of astrophotography and medical imaging technologies. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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17 pages, 5495 KiB

Open AccessArticle

Application of an Output Filtering Method for an Unstable Wheel-Driven Pendulum System Parameter Identification

by Chao-Chung Peng, Nai-Jen Cheng and Min-Che Tsai

Electronics 2023, 12(22), 4569; https://doi.org/10.3390/electronics12224569 - 8 Nov 2023

Cited by 1 | Viewed by 895

Abstract

This research aims to apply an output filtering method to conduct the system parameter identification of an unstable wheel-driven pendulum system. First, the nonlinear dynamic model of the system is established by utilizing the Lagrangian dynamic theorem. Next, the Least-Square (LS) is introduced for system parameter identification formulation. Nevertheless, considering the real scenario, the wheel displacement is acquired from encoders subject to quantization errors. The pitch angle of the pendulum cart is also accompanied by Gaussian noise. Therefore, using numerical differentiation for angular acceleration in the LS estimations directly would induce incorrect state information seriously. To address this practical issue, an output filtering method is considered. The developed parameter identification algorithm could attenuate the influence of the quantization effect as well as noisy data and thus obtain much more accurate parameter identification results. Comparative simulation reveals that the output filtering method has a superior parameter estimation performance than the direct numerical difference method. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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19 pages, 8072 KiB

Open AccessArticle

Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles

by Sun-Ho Jang, Woo-Jin Ahn, Yu-Jin Kim, Hyung-Gil Hong, Dong-Sung Pae and Myo-Taeg Lim

Electronics 2023, 12(18), 3773; https://doi.org/10.3390/electronics12183773 - 6 Sep 2023

Cited by 1 | Viewed by 1202

Abstract

Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like hindsight experience replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines obstacle-dependent Gaussian (ODG) RL, soft actor-critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. With these solutions in place, the ultimate aim of this work is to improve the performance of reinforcement learning and develop a swift, stable, and robust learning method for implementing autonomous driving systems that can effectively adapt to various environments and overcome the constraints of direct RL utilization in autonomous vehicles. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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17 pages, 2726 KiB

Open AccessArticle

A Two-Stage Image Inpainting Technique for Old Photographs Based on Transfer Learning

by Mingju Chen, Zhengxu Duan, Lan Li, Sihang Yi and Anle Cui

Electronics 2023, 12(15), 3221; https://doi.org/10.3390/electronics12153221 - 25 Jul 2023

Cited by 1 | Viewed by 1345

Abstract

To address the challenge of sparse old photo datasets, we apply transfer learning to image inpainting tasks. Specifically, we improve a two-stage image inpainting network that focuses on collaborative subtasks. We also design a transform module based on the cross-aggregation of windows to improve long-distance contextual information acquisition in image inpainting and enhance the integrity of images in terms of structure and texture. Our improved two-stage network has a significantly better repair performance compared to that of the current common inpainting methods. We further apply transfer learning techniques by utilizing the improved two-stage image inpainting network as the base network and decoupling the generator into a feature extractor and classifier, which consist of an encoder and a decoder, respectively. We obtain a domain-invariant feature extractor through minimax game training using source and target domain data. This feature extractor can be combined with the original encoder to restore old photo images. To verify the effectiveness of our approach, we conducted comparative experiments. Our results show that the PSNR, SSIM, and FID indexes of the model using transfer learning are 11.8%, 2.96%, and 44.4% higher than those without transfer learning, respectively. These findings suggest that applying transfer learning techniques can be an effective solution to address the challenge of sparse old photo datasets in image inpainting tasks. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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20 pages, 9710 KiB

Open AccessArticle

Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features

by Ean-Gyu Han, Tae-Koo Kang and Myo-Taeg Lim

Electronics 2023, 12(13), 2933; https://doi.org/10.3390/electronics12132933 - 3 Jul 2023

Cited by 3 | Viewed by 2559

Abstract

This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental results demonstrate that the proposed method surpasses alternative approaches in terms of accuracy and the recognition interval. By extracting common features of the PPG and EMG signals, this approach achieves effective emotion recognition without mutual interference. The findings provide a significant advancement in real-time emotion analysis and offer a clear and concise framework for understanding individuals’ emotional states using physiological signals. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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14 pages, 5945 KiB

Open AccessArticle

A Transformer-Based Cross-Window Aggregated Attentional Image Inpainting Model

by Mingju Chen, Tingting Liu, Xingzhong Xiong, Zhengxu Duan and Anle Cui

Electronics 2023, 12(12), 2726; https://doi.org/10.3390/electronics12122726 - 19 Jun 2023

Cited by 2 | Viewed by 1613

Abstract

To overcome the fault of convolutional networks, which can be over-smooth, blurred, or discontinuous, a novel transformer network with cross-window aggregated attention is proposed. Our network as a whole is constructed as a generative adversarial network model, and by embedding the Window Aggregation Transformer (WAT) module, we improve the information aggregation between windows without increasing the computational complexity and effectively obtain the image long-range dependencies to solve the problem that convolutional operations are limited by local feature extraction. First, the encoder extracts the multi-scale features of the image with convolution kernels of different scales; second, the feature maps of different scales are input into a WAT module to realize the aggregation between feature information and finally, these features are reconstructed by the decoder, and then, the generated image is input into the global discriminator, in which the discrimination between real and fake images is completed. It is experimentally verified that our designed Transformer window attention network is able to make the structured texture of the restored images richer and more natural when performing the restoration task of large broken or structurally complex images. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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23 pages, 5335 KiB

Open AccessArticle

Analysis of the Security Challenges Facing the DS-Lite IPv6 Transition Technology

by Ameen Al-Azzawi and Gábor Lencse

Electronics 2023, 12(10), 2335; https://doi.org/10.3390/electronics12102335 - 22 May 2023

Cited by 4 | Viewed by 2016

Abstract

This paper focuses on one of the most prominent IPv6 transition technologies named DS-Lite (Dual-Stack Lite). The aim was to analyze the security threats to which this technology might be vulnerable. The analysis is based on the STRIDE method, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, and Elevation of Privilege. A testbed was built for the DS-Lite topology using several virtual machines, which were created using CentOS Linux images. The testbed was used to perform several types of attacks against the infrastructure of DS-Lite, especially against the B4 (Basic Bridging Broadband) and the AFTR (Address Family Transition Router) elements, where it was shown that the pool of source ports can be exhausted in 14 s. Eventually, the most common attacks that DS-Lite is susceptible to were summarized, and methods for mitigating such attacks were proposed. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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24 pages, 16929 KiB

Open AccessPerspective

Study on Automatic Electric Vehicle Charging Socket Detection Using ZED 2i Depth Sensor

by Vladimir Tadic

Electronics 2023, 12(4), 912; https://doi.org/10.3390/electronics12040912 - 10 Feb 2023

Cited by 6 | Viewed by 2526

Abstract

This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step in a series of robotic operations, where the intent is to detect and extract the charging socket on the vehicle’s body surface. The ZED 2i depth sensor was utilized for scene recording with artificial illumination. Later, the socket detection and extraction were accomplished using both simple image processing and morphological operations in an object extraction algorithm with tilt angles and centroid coordinates determination of the charging socket itself. The aim was to use well-known, simple, and proven image processing techniques in the proposed method to ensure both reliable and smooth functioning of the robot’s vision system in an industrial environment. The experiments demonstrated that the deployed algorithm both extracts the charging socket and determines the slope angles and socket coordinates successfully under various depth assessment conditions, with a detection rate of 94%. Full article

(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)

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