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Deep Learning Technology and Image Sensing: 2nd Edition
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Deep Learning Technology and Image Sensing: 2nd Edition

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

Deadline for manuscript submissions: 28 November 2024 | Viewed by 1307

Special Issue Editors

Prof. Dr. Sukho Lee

E-Mail Website
Guest Editor
Division of Computer Engineering, Dongseo University, 47 Jurye Road, Sasang Gu, Busan 47011, Republic of Korea
Interests: image deconvolution/restoration; color image compression; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dae-Ki Kang

E-Mail Website1 Website2
Guest Editor
Machine Learning/Deep Learning Research Labs, Department of Computer Engineering, Dongseo University, Busan 47011, Republic of Korea
Interests: automated machine learning; adversarial machine learning; multi-agent reinforcement learning; few shot learning; generative adversarial network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning-based computing technology is significantly improving the quality and reliability of image recognition data today. For example, in the field of autonomous driving, the performance of sensor themselves is also increasing through deep learning based on sensor and data fusion between front camera sensors and radars. Other deep learning-based computer vision technologies help to improve the performance of smartphone camera applications such as face recognition, panorama photography, depth/geometry detection, and high-quality magnification and detection. Still, other computer vision technologies have come to accurately recognize human behavior and posture. This allows for the use of human behavior as a tool for human–computer interfaces (HCI) in applications such as the Metaverse. This Special Issue covers all topics related to applications using deep learning-based image and video sensing technologies. 

Topics include, but are not limited to, the following:

  • Deep learning-based image sensing techniques;
  • Deep learning-based video sensing techniques;
  • Deep learning-based computer vision algorithms;
  • Deep learning-based signal processing techniques;
  • Deep learning-based computational photography.

Prof. Dr. Sukho Lee
Prof. Dr. Dae-Ki Kang
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. 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

  • deep learning
  • image sensing
  • video sensing
  • image sensor
  • video sensor
  • computer vision

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 4076 KiB  
Article
Deep Ensemble Learning-Based Sensor for Flotation Froth Image Recognition
by Xiaojun Zhou and Yiping He
Sensors 2024, 24(15), 5048; https://doi.org/10.3390/s24155048 - 4 Aug 2024
Viewed by 391
Abstract
Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. [...] Read more.
Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners’ predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold–antimony froth flotation application. Full article
(This article belongs to the Special Issue Deep Learning Technology and Image Sensing: 2nd Edition)
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18 pages, 5484 KiB  
Article
ELA-Net: An Efficient Lightweight Attention Network for Skin Lesion Segmentation
by Tianyu Nie, Yishi Zhao and Shihong Yao
Sensors 2024, 24(13), 4302; https://doi.org/10.3390/s24134302 - 2 Jul 2024
Viewed by 652
Abstract
In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing [...] Read more.
In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods. Full article
(This article belongs to the Special Issue Deep Learning Technology and Image Sensing: 2nd Edition)
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