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Electronics
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Application of Neural Networks in Image Classification
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Application of Neural Networks in Image Classification

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 14685

Special Issue Editors

Prof. Dr. Dong-Joong Kang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
Interests: machine vision; object detection and classification based on deep learning; defect inspection in factory process
Prof. Dr. Zhu Teng

E-Mail Website
Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: computer vision; deep learning; machine learning

Special Issue Information

Dear Colleagues,

Artificial neural network (ANNs) research and its applications have grown tremendously in the last decade due to the enhancement of computers’ operational capability, availability of labeled data, and advances in learning algorithms. Classification as a fundamental task has made great strides and been employed in many other tasks. This Special Issue aims to provide a platform for researchers to share state-of-the-art developments in the field. The scope of this Special Issue is deep learning, machine vision, and application of neural networks in a wide range of real-world problems, such as object tracking, web intelligence, remote sensing images, action recognition, and segmentation and classification.

The topics of interests include but are not limited to the following topics:  

  • Image classification based on deep learning algorithms
  • Deep learning theory
  • Deep generative model
  • Multitask, transfer, and meta learning
  • Deep reinforcement learning
  • Image based object tracking using deep learning
  • Anomaly detection
  • Real-world applications based on deep learning
  • Survey for deep learning-based image classification

Prof. Dr. Dong-Joong Kang
Prof. Dr. Zhu Teng
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 (AI) algorithm
  • Deep learning (AI) architecture
  • Deep learning applications
  • Intelligent robots
  • Intelligent systems
  • Machine learning
  • Machine vision
  • Neural networks
  • Parallel processing
  • Web intelligence applications and search

Published Papers (4 papers)

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Research

12 pages, 2149 KiB  
Article
Lane Image Detection Based on Convolution Neural Network Multi-Task Learning
by Junfeng Li, Dehai Zhang, Yu Ma and Qing Liu
Electronics 2021, 10(19), 2356; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10192356 - 27 Sep 2021
Cited by 11 | Viewed by 2959
Abstract
Based on deep neural network multi-task learning technology, lane image detection is studied to improve the application level of driverless technology, improve assisted driving technology and reduce traffic accidents. The lane line database published by Caltech and Tucson company is used to extract [...] Read more.
Based on deep neural network multi-task learning technology, lane image detection is studied to improve the application level of driverless technology, improve assisted driving technology and reduce traffic accidents. The lane line database published by Caltech and Tucson company is used to extract the ROI (Region of Interest), scale, and inverse perspective transformation as well as to preprocess the image, so as to enrich the data set and improve the efficiency of the algorithm. In this study, ZFNet is used to replace the basic networks of VPGNet, and their structures are changed to improve the detection efficiency. Multi-label classification, grid box regression and object mask are used as three task modules to build a multi-task learning network named ZF-VPGNet. Considering that neural networks will be combined with embedded systems in the future, the network will be compressed to CZF-VPGNet without excessively affecting the accuracy. Experimental results show that the vision system of driverless technology in this study achieved good test results. In the case of fuzzy lane line and missing lane line mark, the improved algorithm can still detect and obtain the correct results, and achieves high accuracy and robustness. CZF-VPGNet can achieve high real-time performance (26FPS), and a single forward pass takes about 36 ms or less. Full article
(This article belongs to the Special Issue Application of Neural Networks in Image Classification)
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19 pages, 4730 KiB  
Article
Automatic Detection of Discrimination Actions from Social Images
by Zhihao Wu, Baopeng Zhang, Tianchen Zhou, Yan Li and Jianping Fan
Electronics 2021, 10(3), 325; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10030325 - 30 Jan 2021
Cited by 1 | Viewed by 2141
Abstract
In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first [...] Read more.
In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches. Full article
(This article belongs to the Special Issue Application of Neural Networks in Image Classification)
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15 pages, 1395 KiB  
Article
FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks
by Lino Antoni Giefer, Benjamin Staar and Michael Freitag
Electronics 2020, 9(11), 1824; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111824 - 02 Nov 2020
Cited by 4 | Viewed by 2576
Abstract
Quantization of the weights and activations of a neural network is a way to drastically reduce necessary memory accesses and to replace arithmetic operations with bit-wise operations. This is especially beneficial for the implementation on field-programmable gate array (FPGA) technology that is particularly [...] Read more.
Quantization of the weights and activations of a neural network is a way to drastically reduce necessary memory accesses and to replace arithmetic operations with bit-wise operations. This is especially beneficial for the implementation on field-programmable gate array (FPGA) technology that is particularly suitable for embedded systems due to its low power consumption. In this paper, we propose an in-situ defect detection system utilizing a quantized neural network implemented on an FPGA for an automated surface inspection of wind turbine rotor blades using unpiloted aerial vehicles (UAVs). Contrary to the usual approach of offline defect detection, our approach prevents major downtimes and hence expenses. To our best knowledge, our work is among the first to transfer neural networks with weight and activation quantization into a tangible application. We achieve promising results with our network trained on our dataset consisting of 8024 good and defected rotor blade patches. Compared to a conventional network using floating-point arithmetic, we show that the classification accuracy we achieve is only slightly reduced by approximately 0.6%. With this work, we present a basic system for in-situ defect detection with versatile usability. Full article
(This article belongs to the Special Issue Application of Neural Networks in Image Classification)
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19 pages, 21567 KiB  
Article
Underwater-Sonar-Image-Based 3D Point Cloud Reconstruction for High Data Utilization and Object Classification Using a Neural Network
by Minsung Sung, Jason Kim, Hyeonwoo Cho, Meungsuk Lee and Son-Cheol Yu
Electronics 2020, 9(11), 1763; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111763 - 23 Oct 2020
Cited by 11 | Viewed by 6091
Abstract
This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a [...] Read more.
This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments. Full article
(This article belongs to the Special Issue Application of Neural Networks in Image Classification)
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