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Electronics
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Recent Advances in Object Detection and Image Processing
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Recent Advances in Object Detection and Image Processing

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 3280

Special Issue Editors

Dr. Mingye Ju

E-Mail Website
Guest Editor
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210000, China
Interests: image colour analysis; image enhancement; image restoration; computer vision; image denoising; image fusion; image resolution; Bayes methods; Internet of Things; computational complexity; feature extraction; geophysical image processing; geophysics computing; image segmentation; image texture; infrared imaging; learning (artificial intelligence); matrix algebra; object detection; optimization; parabolic equations; quadtrees; recursive estimation; search problems; seawater
Dr. Shu-Lin Chen

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: reconfigurable antennas; leaky-wave antennas
Dr. Tianyu Yang

E-Mail Website
Guest Editor
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
Interests: THz and optical Micro-structured fiber design and its sensing application

Special Issue Information

Dear Colleagues,

Object detection and image processing are both key research directions in the fields of computer vision and artificial intelligence, which are widely used in pedestrian detection, face recognition, remote sensing detection, and vehicle detection. Advancements in object detection and image processing methods have vital practical significance to reduce the consumption of human capital through computer vision. In recent years, various advanced object detection, image processing, and feature learning methods have been developed, effectively supporting subsequent intelligent understanding, reasoning, and decision tasks. Although the existing object detection and image processing algorithms have achieved some fruitful research results, they can only be applied to simple, clear environments. Many factors, such as insufficient light, bad weather conditions, obstacle occlusion, and cluttered background, pose challenges to the rapid development of visual perception algorithms.

The aim of this Special Issue is to gather papers presenting recent advances in object detection and image processing, which gives researchers the opportunity to provide new tendencies, as well as the latest achievements and research directions, and to represent their current work on the aforementioned problems.

We invite researchers to contribute their original articles and reviews. Topics may include, but are not limited to, the following areas:

  • Advanced object detection models;
  • Advanced image processing models;
  • Deep learning and machine learning models for object detection and image processing;
  • Object detection and image processing semantic segmentation;
  • Online learning for object detection;
  • Digital image enhancement;
  • Image dehazing;
  • Advances in machine vision/computer vision;
  • Image classification and recognition;
  • Neural computing for image processing;
  • Evolutionary algorithms for image processing;
  • Combined upstream and downstream multitasking.

Dr. Mingye Ju
Dr. Shu-Lin Chen
Dr. Tianyu Yang
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

  • object detection
  • image processing
  • computer vision
  • pattern recognition

Published Papers (3 papers)

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Research

16 pages, 4936 KiB  
Article
NGDCNet: Noise Gating Dynamic Convolutional Network for Image Denoising
by Minling Zhu and Zhihai Li
Electronics 2023, 12(24), 5019; https://doi.org/10.3390/electronics12245019 - 15 Dec 2023
Viewed by 674
Abstract
Deep convolution neural networks (CNNs) have become popular for image denoising due to their robust learning capabilities. However, many methods tend to increase the receptive field to improve performance, which leads to over-smoothed results and loss of critical high-frequency information such as edges [...] Read more.
Deep convolution neural networks (CNNs) have become popular for image denoising due to their robust learning capabilities. However, many methods tend to increase the receptive field to improve performance, which leads to over-smoothed results and loss of critical high-frequency information such as edges and texture. In this research, we introduce an innovative end-to-end denoising network named the noise gating dynamic convolutional network (NGDCNet). By integrating dynamic convolution and noise gating mechanisms, our approach effectively reduces noise while retaining finer image details. Through a series of experiments, we conduct a comprehensive evaluation of NGDCNet by comparing it quantitatively and visually against state-of-the-art denoising methods. Additionally, we provide an ablation study to analyze the contributions of dynamic convolutional blocks and noise gating blocks. Our experimental findings demonstrate that NGDCNet excels in noise reduction while preserving essential texture information. Full article
(This article belongs to the Special Issue Recent Advances in Object Detection and Image Processing)
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19 pages, 9083 KiB  
Article
A Deep Learning-Enhanced Stereo Matching Method and Its Application to Bin Picking Problems Involving Tiny Cubic Workpieces
by Masaru Yoshizawa, Kazuhiro Motegi and Yoichi Shiraishi
Electronics 2023, 12(18), 3978; https://doi.org/10.3390/electronics12183978 - 21 Sep 2023
Viewed by 741
Abstract
This paper proposes a stereo matching method enhanced by object detection and instance segmentation results obtained through the use of a deep convolutional neural network. Then, this method is applied to generate a picking plan to solve bin picking problems, that is, to [...] Read more.
This paper proposes a stereo matching method enhanced by object detection and instance segmentation results obtained through the use of a deep convolutional neural network. Then, this method is applied to generate a picking plan to solve bin picking problems, that is, to automatically pick up objects with random poses in a stack using a robotic arm. The system configuration and bin picking process flow are suggested using the proposed method, and it is applied to bin picking problems, especially those involving tiny cubic workpieces. The picking plan is generated by applying the Harris corner detection algorithm to the point cloud in the generated three-dimensional map. In the experiments, two kinds of stacks consisting of cubic workpieces with an edge length of 10 mm or 5 mm are tested for bin picking. In the first bin picking problem, all workpieces are successfully picked up, whereas in the second, the depths of the workpieces are obtained, but the instance segmentation process is not completed. In future work, not only cubic workpieces but also other arbitrarily shaped workpieces should be recognized in various types of bin picking problems. Full article
(This article belongs to the Special Issue Recent Advances in Object Detection and Image Processing)
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19 pages, 65594 KiB  
Article
DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising
by Jialiang Zhang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun and Mingye Ju
Electronics 2023, 12(14), 3038; https://doi.org/10.3390/electronics12143038 - 11 Jul 2023
Cited by 2 | Viewed by 974
Abstract
Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, [...] Read more.
Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, conventional Retinex-based approaches for low-light image enhancement frequently fail to accomplish real denoising. This research introduces DEGANet, a revolutionary deep-learning framework created particularly for improving and denoising low-light images. To overcome these restrictions, DEGANet makes use of the strength of a Generative Adversarial Network (GAN). The Decom-Net, Enhance-Net, and an Adversarial Generative Network (GAN) are three linked subnets that make up our novel Retinex-based DEGANet architecture. The Decom-Net is in charge of separating the reflectance and illumination components from the input low-light image. This decomposition enables Enhance-Net to effectively enhance the illumination component, thereby improving the overall image quality. Due to the complicated noise patterns, fluctuating intensities, and intrinsic information loss in low-light images, denoising them presents a significant challenge. By incorporating a GAN into our architecture, DEGANet is able to effectively denoise and smooth the enhanced image as well as retrieve the original data and fill in the gaps, producing an output that is aesthetically beautiful while maintaining key features. Through a comprehensive set of studies, we demonstrate that DEGANet exceeds current state-of-the-art methods in both terms of image enhancement and denoising quality. Full article
(This article belongs to the Special Issue Recent Advances in Object Detection and Image Processing)
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