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Applied Sciences
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Learning-Based Object and Pattern Recognition
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Learning-Based Object and Pattern Recognition

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 (20 August 2023) | Viewed by 3932

Special Issue Editors

Prof. Dr. Changsheng Li

E-Mail Website
Guest Editor
School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China
Interests: machine learning; including deep learning; self-space learning; active learning; multi-task learning; model compression; meta-learning; etc.; computer vision; including video behavior recognition and detection; scene understanding; etc
Dr. Guibo Zhu

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing, China
Interests: machine learning; object detection; object recognition; object tracking

Special Issue Information

Dear Colleagues,

Object recognition plays an important role in various real-world applications, including autonomous driving, intelligent visual surveillance, etc. Recent years have witnessed the rapid progress of deep neural networks on learning-based object recognition. However, there is still a large room to design more advanced techniques for object recognition. In addition, it remains non-trivial for practitioners to explore how to apply existing works to more practical applications. This special issue seeks submissions about the latest learning-based object and pattern recognition models, methodologies, and applications. It targets both academic researchers and industrial practitioners from computer vision and machine learning communities. Topics of interest include, but are not limited to:

  • Object recognition
  • Active learning for object recognition
  • Multi-task learning for object recognition
  • Deep learning for object recognition
  • Meta-learning for object recognition
  • Online learning for object recognition
  • Model compression for object recognition
  • Reinforcement learning for object recognition
  • Self-supervised learning for object recognition
  • Unsupervised learning for object recognition
  • Graph neural network for object recognition

Prof. Dr. Changsheng Li
Dr. Guibo Zhu
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. 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

  • machine learning
  • deep learning
  • object recognition
  • pattern recognition
  • applications

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Published Papers (2 papers)

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Research

16 pages, 13446 KiB  
Article
DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
by Beomyeon Hwang, Sanghun Lee and Hyunho Han
Appl. Sci. 2023, 13(3), 1841; https://doi.org/10.3390/app13031841 - 31 Jan 2023
Cited by 3 | Viewed by 1762
Abstract
Recent advances in convolutional neural network (CNN)-based object detection have a trade-off between accuracy and computational cost in various industrial tasks and essential consideration. However, the fully convolutional one-stage detector (FCOS) demonstrates low accuracy compared with its computational costs owing to the loss [...] Read more.
Recent advances in convolutional neural network (CNN)-based object detection have a trade-off between accuracy and computational cost in various industrial tasks and essential consideration. However, the fully convolutional one-stage detector (FCOS) demonstrates low accuracy compared with its computational costs owing to the loss of low-level information. Therefore, we propose a module called a dual-path lightweight module (DLM) that efficiently utilizes low-level information. In addition, we propose a DLMFCOS based on DLM to achieve an optimal trade-off between computational cost and detection accuracy. Our network minimizes feature loss by extracting spatial and channel information in parallel and implementing a bottom-up feature pyramid network that improves low-level information detection. Additionally, the structure of the detection head is improved to minimize the computational cost. The proposed method was trained and evaluated by fine-tuning parameters through experiments and using public datasets PASCAL VOC 07 and MS COCO 2017 datasets. The average precision (AP) metric is used for our quantitative evaluation matrix for detection performance, and our model achieves an average 1.5% accuracy improvement at about 33.85% lower computational cost on each dataset than the conventional method. Finally, the efficiency of the proposed method is verified by comparing the proposed method with the conventional method through an ablation study. Full article
(This article belongs to the Special Issue Learning-Based Object and Pattern Recognition)
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12 pages, 1247 KiB  
Article
OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
by Jiaqi Yu, Ye Yuan, Qian Zhang, Wei Zhang, Ziyu Fan and Fusheng Jin
Appl. Sci. 2023, 13(1), 91; https://doi.org/10.3390/app13010091 - 21 Dec 2022
Cited by 3 | Viewed by 1539
Abstract
Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal, OFDM (Orthogonal Frequency Division Multiplexing) signal is of high [...] Read more.
Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal, OFDM (Orthogonal Frequency Division Multiplexing) signal is of high complexity so emitter identification technology under OFDM is extremely challenging. In this paper, an emitter identification method based on contrastive learning and residual network is proposed. First, according to the particularity of OFDM, we adjust the structure of ResNet and propose a targeted data preprocessing method. Then, some data augmentation strategies are designed to construct positive samples. We conduct self-supervised pretraining to distinguish features of positive and negative samples in hidden space. Based on the pretrained feature extractor, the classifier is no longer trained from scratch. Extensive experiments have validated the effectiveness of our proposed methods. Full article
(This article belongs to the Special Issue Learning-Based Object and Pattern Recognition)
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