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Algorithms
Special Issues
Advances in Computer Vision: Emerging Trends and Applications
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Advances in Computer Vision: Emerging Trends and Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 808

Special Issue Editors

Dr. Noman Khan

E-Mail Website
Guest Editor
Department of Software Convergence, Sejong University, Seoul 143-747, Republic of Korea
Interests: energy informatics; computer vision; virtual and augmented reality; bioinformatics; IoT; IIoT; machine learning; deep learning
Dr. Samee Ullah Khan

E-Mail Website
Guest Editor
Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
Interests: person re-identification; bioinformatics; energy informatics; video analytics; computer vision; deep learning; machine learning; activity recognition; video summarization

Special Issue Information

Dear Colleagues,

The rapid growth of big data has transformed the landscape of computer vision, particularly in surveillance applications. With vast amounts of visual data being generated daily, advanced computer vision techniques are essential for extracting actionable insights and enhancing real-time decision making. This Special Issue focuses on applications such as intelligent surveillance systems, behavior analysis, disaster management, anomaly detection, activity recognition, person re-identification, and crowd management. We encourage submissions that tackle the challenges associated with processing and analyzing large-scale visual datasets while addressing the ethical implications of surveillance technologies. This Special Issue aims to advance the understanding and utilization of computer vision in big data by starting a dialogue on innovative methodologies and practical applications.

The topics of interest include, but are not limited to, the following:

  • Intelligent surveillance systems;
  • Behavior analysis and activity recognition;
  • Disaster management and response;
  • Anomaly detection in video streams;
  • Human behavior analysis;
  • Crowd management and safety;
  • Person re-identification in surveillance applications;
  • Object detection and segmentation.

Dr. Noman Khan
Dr. Samee Ullah Khan
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
  • activity recognition
  • intelligent surveillance systems
  • behavior analysis
  • person re-identification
  • object detection

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

17 pages, 6914 KiB  
Article
YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations
by Dong Ding, Zhengrong Deng and Rui Yang
Algorithms 2025, 18(1), 27; https://doi.org/10.3390/a18010027 - 6 Jan 2025
Viewed by 487
Abstract
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in [...] Read more.
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel–spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations. Full article
(This article belongs to the Special Issue Advances in Computer Vision: Emerging Trends and Applications)
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