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Applied Sciences
Special Issues
Object Detection and Pattern Recognition in Image Processing
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Object Detection and Pattern Recognition in Image 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 (29 February 2024) | Viewed by 825

Special Issue Editor

Prof. Dr. Xi Yang

E-Mail Website
Guest Editor
The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: computer vision; machine learning; pattern recognition

Special Issue Information

Dear Colleagues,

Object detection and pattern recognition in image processing has become an increasingly important but challenging research topic in various applications, such as environment monitoring, traffic management, and urban planning.

However, traditional methods are limited due to the complex model design, sensitivity to scale change of objects, low performance for fine-grained understanding tasks, strong dependency on manual labels, and so on.

Thus, this Special Issue aims to explore high-performance object detection and pattern recognition approaches. Areas include (but are not limited to) novel deep learning methods, high-level object understanding tasks, and pattern recognition with machine learning. Additionally, determining how to achieve end-to-end lightweight object detection with strong robustness against noise interference and how to improve pattern recognition performance with a few labels are also topics of interest.

Prof. Dr. Xi Yang
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 for object detection
  • pattern recognition with machine learning
  • lightweight object detection
  • salient object detection
  • fine-grained object recognition
  • semi-supervised/unsupervised learning in image processing

Published Papers (1 paper)

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Research

21 pages, 124895 KiB  
Article
EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
by Indah Monisa Firdiantika, Seongryeong Lee, Chaitali Bhattacharyya, Yewon Jang and Sungho Kim
Appl. Sci. 2024, 14(7), 2763; https://doi.org/10.3390/app14072763 - 26 Mar 2024
Viewed by 425
Abstract
Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can [...] Read more.
Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models. Full article
(This article belongs to the Special Issue Object Detection and Pattern Recognition in Image Processing)
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