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Applied Sciences
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Applications of Machine Learning in Image Recognition and Processing
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Applications of Machine Learning in Image Recognition and Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3787

Special Issue Editor

Prof. Dr. Wu-Chih Hu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
Interests: image processing; pattern recognition; visual surveillance; internet of things; big data analysis; deep learning

Special Issue Information

Dear Colleagues,

Due to the fact that image capture software and hardware technology have advanced and became affordable, various fixed devices (such as road photography, intersection monitoring, parking lot monitoring, etc.) and mobile vehicles (such as smart phones, car cameras, unmanned aerial cameras, etc.) can be highly integrated with the communication network, and images and videos are gradually replacing traditional text and audio as part of people's daily work and leisure. This also encourages more R&D personnel to use their creativity to delve into more advanced technologies and systems of image processing and recognition, allowing a variety of innovative image/video value-added applications to be conceived. Furthermore, based on the rapid development of deep learning in recent years, it has had a great effect on the research of image processing and recognition. There are many research topics related to image processing and recognition, covering a relatively large depth and breadth. Therefore, in terms of practical applications, the fields of image/video processing and recognition have experienced substantial growth in the past ten years.

The Special Issue on “Applications of Machine Learning in Image Recognition and Processing” covers the innovative technologies or applications of rising trends in image processing and recognition. Topics of interest include, but are not limited to, the following:

  • Small target detection and tracking recognition;
  • Crowd counting;
  • Biomedical image processing
  • Intelligent agriculture;
  • Intelligent aquaculture;
  • Intelligent transportation;
  • Intelligent surveillance;
  • Forgery detection and recognition.

Prof. Dr. Wu-Chih Hu
Guest Editor

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. Applied Sciences 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 learning
  • image processing
  • image recognition
  • intelligent application

Published Papers (3 papers)

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Research

16 pages, 5387 KiB  
Article
Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
by Jun Zhang, Rongxi Zhang, Xinming Shu, Lulu Yu and Xuanning Xu
Appl. Sci. 2023, 13(18), 10202; https://doi.org/10.3390/app131810202 - 11 Sep 2023
Cited by 1 | Viewed by 1086
Abstract
The identification of trolley codes poses a challenge in engineering, as there are often situations where the accuracy requirements for their detection cannot be met. YOLOv7, being the state-of-the-art target detection method, demonstrates significant efficacy in addressing the challenge of trolley coding recognition. [...] Read more.
The identification of trolley codes poses a challenge in engineering, as there are often situations where the accuracy requirements for their detection cannot be met. YOLOv7, being the state-of-the-art target detection method, demonstrates significant efficacy in addressing the challenge of trolley coding recognition. Due to the substantial dimensions of the model and the presence of numerous redundant parameters, the deployment of small terminals in practical applications is constrained. This paper presents a real-time approach for identifying trolley codes using a YOLOv7 deep learning algorithm that incorporates channel pruning. Initially, a YOLOv7 model is constructed, followed by the application of a channel pruning algorithm to streamline its complexity. Subsequently, the model undergoes fine-tuning to optimize its performance in terms of both speed and accuracy. The experimental findings demonstrated that the proposed model exhibited a reduction of 32.92% in the number of parameters compared to the pre-pruned model. Additionally, it was observed that the proposed model was 24.82 MB smaller in size. Despite these reductions, the mean average precision (mAP) of the proposed model was only 0.03% lower, reaching an impressive value of 99.24%. We conducted a comparative analysis of the proposed method against five deep learning algorithms, namely YOLOv5x, YOLOv4, YOLOv5m, YOLOv5s, and YOLOv5n, in order to assess its effectiveness. In contrast, the proposed method considers the speed of detection while simultaneously ensuring a high mean average precision (mAP) value in the detection of trolley codes. The obtained results provide confirmation that the suggested approach is viable for the real-time detection of trolley codes. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Image Recognition and Processing)
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18 pages, 9308 KiB  
Article
A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
by Juhao Gao, Ni Tang and Dongxiao Zhang
Appl. Sci. 2023, 13(14), 8110; https://doi.org/10.3390/app13148110 - 12 Jul 2023
Cited by 1 | Viewed by 1135
Abstract
Face verification and recognition are important tasks that have made great progress in recent years. However, recognizing low-resolution faces from small images is still a difficult problem. In this paper, we advocate using diffusion models (DMs) to enhance face resolution and improve their [...] Read more.
Face verification and recognition are important tasks that have made great progress in recent years. However, recognizing low-resolution faces from small images is still a difficult problem. In this paper, we advocate using diffusion models (DMs) to enhance face resolution and improve their quality for various downstream applications. Most existing DMs for super-resolution use U-Net as their backbone network, which only exploits multi-scale features along the spatial dimension. These approaches result in a slow convergence of corresponding DMs and the inability to capture complex details and fine textures. To address this issue, we propose a novel conditional generative model based on DMs called BPSR3, which replaces the U-Net in super-resolution via repeated refinement (SR3) with a multi-scale deep back-projection network structure. BPSR3 can extract richer features not only in depth but also in breadth. This helps to effectively refine the image quality at different scales. The experimental results on facial datasets show that BPSR3 significantly improved both convergence speed and reconstruction performance. BPSR3 has about 1/4 of the parameters of SR3 but achieves a 50.1% improvement in PSNR, a 19.8% improvement in SSIM, and a 15.4% reduction in FID. Our contribution lies in achieving less time and space consumption and better reconstruction results. In addition, we propose an idea of enhancing the performance of DMs by replacing the U-Net with a better network. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Image Recognition and Processing)
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18 pages, 10470 KiB  
Article
An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
by Chishe Wang, Yuting Chen, Jie Wang and Jinjin Qian
Appl. Sci. 2023, 13(12), 7174; https://doi.org/10.3390/app13127174 - 15 Jun 2023
Cited by 1 | Viewed by 1152
Abstract
Traffic congestion detection based on vehicle detection and tracking algorithms is one of the key technologies for intelligent transportation systems. However, in expressway surveillance scenarios, small vehicle size and vehicle occlusion present severe challenges for this method, including low vehicle detection accuracy and [...] Read more.
Traffic congestion detection based on vehicle detection and tracking algorithms is one of the key technologies for intelligent transportation systems. However, in expressway surveillance scenarios, small vehicle size and vehicle occlusion present severe challenges for this method, including low vehicle detection accuracy and low traffic congestion detection accuracy. To address these challenges, this paper proposes an improved version of the CrowdDet algorithm by introducing the Involution operator and bi-directional feature pyramid network (BiFPN) module, which is called IBCDet. The proposed IBCDet module can achieve higher vehicle detection accuracy in expressway surveillance scenarios by enabling long-distance information interaction and multi-scale feature fusion. Additionally, a vehicle-tracking algorithm based on IBCDet is designed to calculate the running speed of vehicles, and it uses the average running speed to achieve traffic congestion detection according to the Chinese expressway level of serviceability (LoS) criteria. Adequate experiments are conducted on both the self-built Nanjing Raoyue expressway monitoring video dataset (NJRY) and the public dataset UA-DETRAC. The experimental results demonstrate that the proposed IBCDet outperforms the commonly used object detection algorithms in both vehicle detection accuracy and traffic congestion detection accuracy. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Image Recognition and Processing)
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