Application of Machine Learning in Graphics and Images, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 803

Special Issue Editors

School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: computer graphics; computer-aided design; computer vision and computer-supported cooperative work
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: intelligent optimization; medical image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science & Engineering, Artificial Intelligence, Wuhan Institute of Technology, Wuhan 430205, China
Interests: object pose estimation; human activity analysis; 3D object detection; point cloud processing

Special Issue Information

Dear Colleagues,

Computer graphics and image processing technologies have been widely used in production processes in society today, as well as other aspects of daily life, offering solutions with greatly improved efficiency and quality. Meanwhile, the last few decades have witnessed machine learning modes becoming effective and ubiquitous approaches applied to various challenging real-world or virtual tasks. Both the fields of image processing and computer graphics are important machine learning application scenarios that have stimulated high research interest and brought about a series of popular research directions.

In this Special Issue, we look forward to your novel research papers or comprehensive surveys of state-of-the-art works that may contribute to innovative machine learning application models, improvements to classical computer graphics and image processing tasks, and new interesting applications. Topics of interest include all aspects of the application of machine learning to graphics and images, but are not limited to the following detailed list:

  • Computer graphics;
  • Image processing;
  • Computer vision;
  • Machine learning and deep learning;
  • Pattern recognition;
  • Object detection, recognition, and tracking;
  • Part and semantic segmentation;
  • Rigid and non-rigid registration;
  • 3D reconstruction;
  • Virtual reality/augmented reality/mixed reality;
  • Computer-aided design/engineering;
  • Human pose and behavior understanding;
  • Autonomous driving.

Dr. Yiqi Wu
Dr. Yilin Chen
Dr. Lu Zou
Guest Editors

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Keywords

  • computer graphics
  • image processing
  • computer vision
  • machine learning
  • deep learning
  • pattern recognition

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Published Papers (1 paper)

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Research

19 pages, 17496 KiB  
Article
HR-YOLO: A Multi-Branch Network Model for Helmet Detection Combined with High-Resolution Network and YOLOv5
by Yuanfeng Lian, Jing Li, Shaohua Dong and Xingtao Li
Electronics 2024, 13(12), 2271; https://doi.org/10.3390/electronics13122271 - 10 Jun 2024
Viewed by 594
Abstract
Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep [...] Read more.
Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
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