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Advances and Applications of Computer Vision in Electronics

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 8324

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Special Issue Editors

Dr. Taehyeon Kim

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Guest Editor

Contents Convergence Research Center, Korea Electronics Technology Institute, Seoul 03924, Korea
Interests: artificial intelligence; computer vision; deep learning; machine learning; pattern recognition; signal processing

Prof. Dr. Yoonsik Choe

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Guest Editor

Department of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Korea
Interests: artificial intelligence; computer vision; video coding; statistical signal processing; digital image processing

Special Issue Information

Dear Colleagues,

Recently, deep learning has achieved remarkable results in various applications. In particular, computer vision technologies using deep learning have achieved rapid technological improvement by overcoming various problems that conventional computer vision methods have found difficult or not solved. The development of computer vision technology has led to new businesses such as autonomous vehicles, metaverse, and smart factory, resulting in an opportunity for various electronic engineering technologies to converge.

Computer vision technologies are expanding from neural architecture research, such as a vision transformer, to 3D computer graphics, such as neural radiance fields. Therefore, this Special Issue aims to provide a unique academic platform for publishing high-quality papers dealing with advances and applications of computer vision technology in electronics. Contributors may write about one of the subjects listed below, but they are not limited to them.

Machine Learning-based Computer Vision
Deep Learning-based Computer Vision
Efficient Computer Vision on Edge Devices
Data Processing for Computer Vision
3D Computer Vision
Applications (Autonomous Vehicles, Metaverse, Smart Factory, 3D Rendering, etc.)

Dr. Taehyeon Kim
Prof. Dr. Yoonsik Choe
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

artificial intelligence
deep learning
machine learning
computer vision
representation learning
supervised learning
self-/semi-/un-supervised learning
generative learning
image processing
3D computer vision
autonomous vehicle
metaverse
virtual/augmented/mixed reality

Published Papers (6 papers)

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Research

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14 pages, 5981 KiB

Open AccessArticle

CD-MAE: Contrastive Dual-Masked Autoencoder Pre-Training Model for PCB CT Image Element Segmentation

by Baojie Song, Jian Chen, Shuhao Shi, Jie Yang, Chen Chen, Kai Qiao and Bin Yan

Electronics 2024, 13(6), 1006; https://doi.org/10.3390/electronics13061006 - 7 Mar 2024

Viewed by 551

Abstract

Element detection is an important step in the process of the non-destructive testing of printed circuit boards (PCB) based on computed tomography (CT). Compared with the traditional manual detection method, the image semantic segmentation method based on deep learning greatly improves efficiency and accuracy. However, semantic segmentation models often require a large amount of data for supervised training to generalize better model performance. Unlike natural images, the PCB CT image annotation task is more time-consuming and laborious than the semantic segmentation task. In order to reduce the cost of labeling and improve the ability of the model to utilize unlabeled data, unsupervised pre-training is a very reasonable and necessary choice. The masked image reconstruction model represented by a masked autoencoder is pre-trained on the unlabeled data, learning a strong feature representation ability by recovering the masked image, and shows a good generalization ability in various downstream tasks. In the PCB CT image element segmentation task, considering the characteristics of the image, it is necessary to use a model with strong feature robustness in the pre-training stage to realize the representation learning on a large number of unlabeled PCB CT images. Based on the above purposes, we proposed a contrastive dual-masked autoencoder (CD-MAE) pre-training model, which can learn more robust feature representation on unlabeled PCB CT images. Our experiments show that the CD-MAE outperforms the baseline model and fully supervised models in the PCB CT element segmentation task. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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15 pages, 6272 KiB

Open AccessArticle

A Negative Emotion Recognition System with Internet of Things-Based Multimodal Biosignal Data

by Seung-Mi Ham, Hye-Min Lee, Jae-Hyun Lim and Jeongwook Seo

Electronics 2023, 12(20), 4321; https://doi.org/10.3390/electronics12204321 - 18 Oct 2023

Viewed by 1407

Abstract

Previous studies to recognize negative emotions for mental healthcare have used heavy equipment directly attaching electroencephalogram (EEG) electrodes to the head, and they have proposed binary classification methods to identify negative emotions. To tackle this problem, we propose a negative emotion recognition system to collect multimodal biosignal data such as five EEG signals from an EEG headset and heart rate, galvanic skin response, and skin temperature from a smart band for classifying multiple negative emotions. This consists of an Android Internet of Things (IoT) application, a oneM2M-compliant IoT server, and a machine learning server. The Android IoT application uploads the biosignal data to the IoT server. By using the biosignal data stored in the IoT server, the machine learning server recognizes the negative emotions of disgust, fear, and sadness using a multiclass support vector machine (SVM) model with a radial basis function kernel. The experimental results demonstrate that the multimodal biosignal data approach achieves 93% accuracy. Moreover, when considering only data from the smart band, the system achieved 98% accuracy by optimizing the hyperparameters of the multiclass SVM model. Based on these results, we plan to develop a metaverse system that detects and expresses negative emotions in real time. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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15 pages, 4402 KiB

Open AccessEditor’s ChoiceArticle

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

by Qiang Liu, Wei Huang, Xiaoqiu Duan, Jianghao Wei, Tao Hu, Jie Yu and Jiahuan Huang

Electronics 2023, 12(18), 3892; https://doi.org/10.3390/electronics12183892 - 15 Sep 2023

Cited by 4 | Viewed by 1782

Abstract

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The [email protected] and [email protected]:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm [email protected] and [email protected]:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% [email protected] and [email protected]:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm [email protected] and [email protected]:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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13 pages, 1750 KiB

Open AccessArticle

Traffic Sign Recognition Based on Bayesian Angular Margin Loss for an Autonomous Vehicle

by Taehyeon Kim, Seho Park and Kyoungtaek Lee

Electronics 2023, 12(14), 3073; https://doi.org/10.3390/electronics12143073 - 14 Jul 2023

Viewed by 864

Abstract

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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18 pages, 7918 KiB

Open AccessArticle

SiamPRA: An Effective Network for UAV Visual Tracking

by Jiafeng Li, Kang Zhang, Zheng Gao, Liheng Yang and Li Zhuo

Electronics 2023, 12(11), 2374; https://doi.org/10.3390/electronics12112374 - 24 May 2023

Cited by 3 | Viewed by 964

Abstract

The visual navigation system is an important module in intelligent unmanned aerial vehicle (UAV) systems as it helps to guide them autonomously by tracking visual targets. In recent years, tracking algorithms based on Siamese networks have demonstrated outstanding performance. However, their application to UAV systems has been challenging due to the limited resources available in such systems.This paper proposes a simple and efficient tracking network called the Siamese Pruned ResNet Attention (SiamPRA) network and applied to embedded platforms that can be deployed on UAVs. SiamPRA is base on the SiamFC network and incorporates ResNet-24 as its backbone. It also utilizes the spatial-channel attention mechanism, thereby achieving higher accuracy while reducing the number of computations. Further, sparse training and pruning are used to reduce the size of the model while maintaining high precision. Experimental results on the challenging benchmarks VOT2018, UAV123 and OTB100 show that SiamPRA has a higher accuracy and lower complexity than other tracking networks. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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16 pages, 3630 KiB

Open AccessTechnical Note

A Novel DME-YOLO Structure in a High-Frequency Transformer Improves the Accuracy and Speed of Detection

by Zhiqiang Kang, Wenqian Jiang, Lile He and Chenrui Zhang

Electronics 2023, 12(18), 3982; https://doi.org/10.3390/electronics12183982 - 21 Sep 2023

Cited by 1 | Viewed by 1122

Abstract

Traditional YOLO models face a dilemma when it comes to dim detection targets: the detection accuracy increases while the speed inevitably reduces, or vice versa. To resolve this issue, we propose a novel DME-YOLO model, which is characterized by the establishment of a backbone based on the YOLOv7 and Dense blocks. Moreover, through the application of feature multiplexing, both the parameters and floating-point computation were decreased; therefore, the defect detection process was accelerated. We also designed a multi-source attention mechanism module called MSAM, which is capable of integrating spatial information from multiple sources. Due to its outstanding quality, the addition of MSAM as the neck of the original YOLOv7 model compensated for the loss of spatial information in the process of forward propagation, thereby improving the detection accuracy of small target defects and simultaneously ensuring real-time detection. Finally, EIOU was adopted as a loss function to bolster the target frame regression process. The results of the experiment indicated detection accuracy and speed values of up to 97.6 mAP and 51.2 FPS, respectively, suggesting the superiority of the model. Compared with the YOLOv7 model, the experimental parameters for the novel DME-YOLO increased by 2.8% for mAP and 15.7 for FPS, respectively. In conclusion, the novel DME-YOLO model had excellent overall performance regarding detection speed and accuracy. Full article

(This article belongs to the Special Issue Advances and Applications of Computer Vision in Electronics)

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