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Electronics
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New Trends in Deep Learning for Computer Vision
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New Trends in Deep Learning for Computer Vision

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 18527

Special Issue Editors

Prof. Dr. Cătălin Căleanu

E-Mail Website
Guest Editor
Department of Communications, Politehnica University Timisoara, 300223 Timisoara, Romania
Interests: deep learning; deep neural networks; computational intelligence; image processing; computer vision; Pattern Recognition; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chih-Hsien Hsia

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Ilan University, Yilan City 260, Taiwan
Interests: DSP IC design; computer vision; image processing; cognitive learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep neural networks (DNNs) and their associated learning paradigm deep learning (DL) currently represent key artificial intelligence (AI) paradigms. There are several reasons for this, including their capacity to learn important features directly from the data, without an explicit and manually defined feature extraction phase. Multiple studies confirm that DNNs are offering the best solutions in many domains, including automotive, biometrics, robotics, cloud computing, medicine, manufacturing, and smart agriculture, to name just a few.

Humans are known to excel in computer vision (CV) tasks. Artificial NNs are loosely inspired by the human brain, having a hierarchical deep multi-layer structure, and are thus expected to provide relatively similar performances. Current research shows that among the most successful DL applications are those which utilize a wide range of neural architectures and learning algorithms in implementing CV operations, such as semantic segmentation, object detection, tracking, reconstruction, synthesis, prediction, perception, and classification.

Motivated by the fast dynamics of DL for the CV field, you are invited to contribute to a Special Issue of Electronics covering recent progress and achievements in utilizing deep learning for computer vision tasks.

Prof. Dr. Cătălin Căleanu
Prof. Dr. Chih-Hsien Hsia
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

  • deep neural network architectures
  • computer vision
  • semantic segmentation
  • object detection
  • tracking
  • prediction
  • perception
  • classification

Published Papers (5 papers)

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Research

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13 pages, 2343 KiB  
Article
All-Weather Pedestrian Detection Based on Double-Stream Multispectral Network
by Chih-Hsien Hsia, Hsiao-Chu Peng and Hung-Tse Chan
Electronics 2023, 12(10), 2312; https://doi.org/10.3390/electronics12102312 - 20 May 2023
Cited by 2 | Viewed by 1131
Abstract
Recently, advanced driver assistance systems (ADAS) have attracted wide attention in pedestrian detection for using the multi-spectrum generated by multi-sensors. However, it is quite challenging for image-based sensors to perform their tasks due to instabilities such as light changes, object shading, or weather [...] Read more.
Recently, advanced driver assistance systems (ADAS) have attracted wide attention in pedestrian detection for using the multi-spectrum generated by multi-sensors. However, it is quite challenging for image-based sensors to perform their tasks due to instabilities such as light changes, object shading, or weather conditions. Considering all the above, based on different spectral information of RGB and thermal images, this study proposed a deep learning (DL) framework to improve the problem of confusing light sources and extract highly differentiated multimodal features through multispectral fusion. Pedestrian detection methods, including a double-stream multispectral network (DSMN), were used to extract a multispectral fusion and double-stream detector with Yolo-based (MFDs-Yolo) information. Moreover, a self-adaptive multispectral weight adjustment method improved illumination–aware network (i-IAN) for later fusion strategy, making different modes complimentary. According to the experimental results, the good performance of this detection method was demonstrated in the public dataset KAIST and the multispectral pedestrian detection dataset FLIR, and it even performed better than the most advanced method in the miss rate (MR) (IoU@0.75) evaluation system. Full article
(This article belongs to the Special Issue New Trends in Deep Learning for Computer Vision)
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12 pages, 3122 KiB  
Article
TransAttention U-Net for Semantic Segmentation of Poppy
by Zifei Luo, Wenzhu Yang, Ruru Gou and Yunfeng Yuan
Electronics 2023, 12(3), 487; https://doi.org/10.3390/electronics12030487 - 17 Jan 2023
Cited by 4 | Viewed by 1312
Abstract
This work represents a new attempt to use drone aerial photography to detect illegal cultivation of opium poppy. The key of this task is the precise segmentation of the poppy plant from the captured image. To achieve segmentation mask close to real data, [...] Read more.
This work represents a new attempt to use drone aerial photography to detect illegal cultivation of opium poppy. The key of this task is the precise segmentation of the poppy plant from the captured image. To achieve segmentation mask close to real data, it is necessary to extract target areas according to different morphological characteristics of poppy plant and reduce complex environmental interference. Based on RGB images, poppy plants, weeds, and background regions are separated individually. Firstly, the pixel features of poppy plant are enhanced using a hybrid strategy approach to augment the too-small samples. Secondly, the U-Shape network incorporating the self-attention mechanism is improved to segment the enhanced dataset. In this process, the multi-head self-attention module is enhanced by using relative position encoding to deal with the special morphological characteristics between poppy stem and fruit. The results indicated that the proposed method can segmented out the poppy plant precisely. Full article
(This article belongs to the Special Issue New Trends in Deep Learning for Computer Vision)
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12 pages, 19431 KiB  
Article
A Novel Deep Learning Model Compression Algorithm
by Ming Zhao, Meng Li, Sheng-Lung Peng and Jie Li
Electronics 2022, 11(7), 1066; https://doi.org/10.3390/electronics11071066 - 28 Mar 2022
Cited by 6 | Viewed by 2999
Abstract
In order to solve the problem of large model computing power consumption, this paper proposes a novel model compression algorithm. Firstly, this paper proposes an interpretable weight allocation method for the loss between a student network (a network model with poor performance), a [...] Read more.
In order to solve the problem of large model computing power consumption, this paper proposes a novel model compression algorithm. Firstly, this paper proposes an interpretable weight allocation method for the loss between a student network (a network model with poor performance), a teacher network (a network model with better performance) and real label. Then, different from the previous simple pruning and fine-tuning, this paper performs knowledge distillation on the pruned model, and quantifies the residual weights of the distilled model. The above operations can further reduce the model size and calculation cost while maintaining the model accuracy. The experimental results show that the weight allocation method proposed in this paper can allocate a relatively appropriate weight to the teacher network and real tags. On the cifar-10 dataset, the pruning method combining knowledge distillation and quantization can reduce the memory size of resnet32 network model from 3726 KB to 1842 KB, and the accuracy can be kept at 93.28%, higher than the original model. Compared with similar pruning algorithms, the model accuracy and operation speed are greatly improved. Full article
(This article belongs to the Special Issue New Trends in Deep Learning for Computer Vision)
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Review

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21 pages, 949 KiB  
Review
A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor
by Ioan Lucan Orășan, Ciprian Seiculescu and Cătălin Daniel Căleanu
Electronics 2022, 11(16), 2545; https://doi.org/10.3390/electronics11162545 - 14 Aug 2022
Cited by 8 | Viewed by 5049
Abstract
Deep neural networks have recently become increasingly used for a wide range of applications, (e.g., image and video processing). The demand for edge inference is growing, especially in the areas of relevance to the Internet-of-Things. Low-cost microcontrollers as edge devices are a promising [...] Read more.
Deep neural networks have recently become increasingly used for a wide range of applications, (e.g., image and video processing). The demand for edge inference is growing, especially in the areas of relevance to the Internet-of-Things. Low-cost microcontrollers as edge devices are a promising solution for optimal application systems from several points of view such as: cost, power consumption, latency, or real-time execution. The implementation of these systems has become feasible due to the advanced development of hardware architectures and DSP capabilities, while the cost and power consumption have been maintained at a low level. The aim of the paper is to provide a literature review on the implementation of deep neural networks using ARM Cortex-M core-based low-cost microcontrollers. As an emerging research direction, there are a limited number of publications that address this topic at the moment. Therefore, the research papers that stand out have been analyzed in greater detail, to promote further interest of researchers to bring AI techniques to low power standard ARM Cortex-M microcontrollers. The article addresses a niche research domain. Despite the increasing interest manifested toward both (1) edge AI applications and (2) theoretical contributions in DNN optimization and compression, the number of existing publications dedicated to the current topic is rather limited. Therefore, a comprehensive literature survey using systematic mapping is not possible. The presentation focuses on systems that have shown increased efficiency in resource-constrained applications, as well as the predominant impediments that still hinder their implementation. The reader will take away the following concepts from this paper: (1) an overview of applications, DNN architectures, and results obtained using ARM Cortex-M core-based microcontrollers, (2) an overview of low-cost hardware devices and SW development solutions, and (3) understanding recent trends and opportunities. Full article
(This article belongs to the Special Issue New Trends in Deep Learning for Computer Vision)
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28 pages, 1948 KiB  
Review
A Contemporary Review on Utilizing Semantic Web Technologies in Healthcare, Virtual Communities, and Ontology-Based Information Processing Systems
by Senthil Kumar Narayanasamy, Kathiravan Srinivasan, Yuh-Chung Hu, Satish Kumar Masilamani and Kuo-Yi Huang
Electronics 2022, 11(3), 453; https://doi.org/10.3390/electronics11030453 - 03 Feb 2022
Cited by 18 | Viewed by 6371
Abstract
The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth [...] Read more.
The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks. Full article
(This article belongs to the Special Issue New Trends in Deep Learning for Computer Vision)
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