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Electronics
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Computer Vision and Image Processing in Machine Learning
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Computer Vision and Image Processing in Machine Learning

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

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 866

Special Issue Editors

Dr. Yuanqi Su

E-Mail Website
Guest Editor
Affiliation School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
Interests: computer vision; pattern recognition
Dr. Chi Zhang

E-Mail Website
Guest Editor
Affiliation Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
Interests: computer vision; pattern recognition

Special Issue Information

Dear Colleagues,

We are delighted to invite you to contribute to a Special Issue on "Computer Vision and Image Processing in Machine Learning" for our journal. This Special Issue aims to provide a platform for showcasing the latest advancements, innovative solutions, and emerging trends in the integration of image processing and computer vision techniques with machine learning approaches.

The field of image processing and computer vision has experienced a remarkable transformation with the advent of powerful machine learning algorithms. These synergistic developments have led to breakthroughs in various applications, including but not limited to:

  • Object detection and recognition;
  • Semantic segmentation;
  • Image restoration and enhancement;
  • Human pose estimation;
  • Autonomous navigation and robotics;
  • Medical image analysis;
  • Satellite and aerial image interpretation;
  • Augmented and virtual reality;
  • Emotion and facial expression analysis.

We invite original research articles, review papers, and insightful case studies that address the theoretical, methodological, and practical aspects of this exciting intersection between image processing, computer vision, and machine learning. Potential topics of interest include, but are not limited to:

  • Deep learning architectures for image and video processing;
  • Unsupervised and semi-supervised learning for computer vision;
  • Transfer learning and domain adaptation in visual tasks;
  • Multimodal fusion and cross-modal learning;
  • Explainable and interpretable machine learning for image analysis;
  • Hardware acceleration and efficient deployment of vision models;
  • Ethical and privacy-preserving considerations in visual AI;
  • Applications of image/video ML in healthcare, autonomous systems, and smart cities.

We encourage researchers, practitioners, and industry experts to contribute their valuable work to this Special Issue. Submissions should follow the journal's guidelines and will undergo a rigorous peer-review process.

The deadline for manuscript submission is 20 May 2025. We look forward to receiving your contributions and working together to advance the state-of-the-art in this exciting field.

Dr. Yuanqi Su
Dr. Chi Zhang
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

  • computer vision
  • machine vision
  • deep learning
  • artificial intelligence

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

22 pages, 6129 KiB  
Article
A Novel Machine Vision-Based Collision Risk Warning Method for Unsignalized Intersections on Arterial Roads
by Zhongbin Luo, Yanqiu Bi, Qing Ye, Yong Li and Shaofei Wang
Electronics 2025, 14(6), 1098; https://doi.org/10.3390/electronics14061098 - 11 Mar 2025
Viewed by 563
Abstract
To address the critical need for collision risk warning at unsignalized intersections, this study proposes an advanced predictive system combining YOLOv8 for object detection, Deep SORT for tracking, and Bi-LSTM networks for trajectory prediction. To adapt YOLOv8 for complex intersection scenarios, several architectural [...] Read more.
To address the critical need for collision risk warning at unsignalized intersections, this study proposes an advanced predictive system combining YOLOv8 for object detection, Deep SORT for tracking, and Bi-LSTM networks for trajectory prediction. To adapt YOLOv8 for complex intersection scenarios, several architectural enhancements were incorporated. The RepLayer module replaced the original C2f module in the backbone, integrating large-kernel depthwise separable convolution to better capture contextual information in cluttered environments. The GIoU loss function was introduced to improve bounding box regression accuracy, mitigating the issues related to missed or incorrect detections due to occlusion and overlapping objects. Furthermore, a Global Attention Mechanism (GAM) was implemented in the neck network to better learn both location and semantic information, while the ReContext gradient composition feature pyramid replaced the traditional FPN, enabling more effective multi-scale object detection. Additionally, the CSPNet structure in the neck was substituted with Res-CSP, enhancing feature fusion flexibility and improving detection performance in complex traffic conditions. For tracking, the Deep SORT algorithm was optimized with enhanced appearance feature extraction, reducing the identity switches caused by occlusions and ensuring the stable tracking of vehicles, pedestrians, and non-motorized vehicles. The Bi-LSTM model was employed for trajectory prediction, capturing long-range dependencies to provide accurate forecasting of future positions. The collision risk was quantified using the predictive collision risk area (PCRA) method, categorizing risks into three levels (danger, warning, and caution) based on the predicted overlaps in trajectories. In the experimental setup, the dataset used for training the model consisted of 30,000 images annotated with bounding boxes around vehicles, pedestrians, and non-motorized vehicles. Data augmentation techniques such as Mosaic, Random_perspective, Mixup, HSV adjustments, Flipud, and Fliplr were applied to enrich the dataset and improve model robustness. In real-world testing, the system was deployed as part of the G310 highway safety project, where it achieved a mean Average Precision (mAP) of over 90% for object detection. Over a one-month period, 120 warning events involving vehicles, pedestrians, and non-motorized vehicles were recorded. Manual verification of the warnings indicated a prediction accuracy of 97%, demonstrating the system’s reliability in identifying potential collisions and issuing timely warnings. This approach represents a significant advancement in enhancing safety at unsignalized intersections in urban traffic environments. Full article
(This article belongs to the Special Issue Computer Vision and Image Processing in Machine Learning)
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