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AI-Based Computer Vision Sensors & Systems—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 119

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Xidian University, Xi'an, China
Interests: visual cognitive computing; computer vision; visual big data mining; intelligent algorithms
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Guest Editor Assistant
Research Institute of Electrical Communication, Tohoku University, Sendai, Miyagi, Japan
Interests: spatial mechanisms of human visual attention; size tuning; cognitive science; LLM for psychology; explainable human–AI interaction systems

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in computer vision sensors and systems is a specialized field that encompasses both current and historical AI advancements, as well as their potential impact and future prospects within sensor technology and its applications. This Special Issue explores the innovative landscape of AI-based computer vision sensors and systems, emphasizing their transformative potential across a variety of applications. These technologies harness advanced imaging techniques to facilitate real-time analysis and intelligent decision-making. We invite researchers to submit original articles investigating the use of RGB cameras, depth cameras (e.g., LiDAR), and thermal cameras in conjunction with image processing units (GPUs, TPUs, FPGAs) and object detection frameworks (e.g., YOLO, SSD, Faster R-CNN) in areas such as environmental monitoring, healthcare imaging, autonomous navigation, and security systems. This Issue aims to highlight innovative methodologies that enhance object detection, gesture recognition, and real-time analytics, ultimately advancing the capabilities of computer vision.

Prof. Dr. Xuefeng Liang
Guest Editor

Dr. Guangyu Chen
Guest Editor Assistant

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Keywords

  • RGB cameras
  • depth cameras (LiDAR)
  • thermal cameras
  • image processing units (GPUs, TPUs, FPGAs)
  • YOLO (You Only Look Once)
  • gesture recognition systems
  • autonomous navigation systems
  • augmented reality (AR)
  • industrial automation
  • smart surveillance systems

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Published Papers (1 paper)

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Research

19 pages, 3920 KB  
Article
HCDFI-YOLOv8: A Transmission Line Ice Cover Detection Model Based on Improved YOLOv8 in Complex Environmental Contexts
by Lipeng Kang, Feng Xing, Tao Zhong and Caiyan Qin
Sensors 2025, 25(17), 5421; https://doi.org/10.3390/s25175421 - 2 Sep 2025
Abstract
When unmanned aerial vehicles (UAVs) perform transmission line ice cover detection, it is often due to the variable shooting angle and complex background environment, which leads to difficulties such as poor ice-covering recognition accuracy and difficulty in accurately identifying the target. To address [...] Read more.
When unmanned aerial vehicles (UAVs) perform transmission line ice cover detection, it is often due to the variable shooting angle and complex background environment, which leads to difficulties such as poor ice-covering recognition accuracy and difficulty in accurately identifying the target. To address these issues, this study proposes an improved icing detection model based on HCDFI–You Only Look Once version 8 (HCDFI-YOLOv8). First, a cross-dense hybrid (CDH) parallel heterogeneous convolutional module is proposed, which can not only improve the detection accuracy of the model, but also effectively alleviate the problem of the surge in the number of floating-point operations during the improvement of the model. Second, deep and shallow feature weighted fusion using improved CSPDarknet53 to 2-Stage FPN_Dynamic Feature Fusion (C2f_DFF) module is proposed to reduce feature loss in neck networks. Third, optimization of the detection head using the feature adaptive spatial feature fusion (FASFF) detection head module is performed to enhance the model’s ability to extract features at different scales. Finally, a new inner-complete intersection over union (Inner_CIoU) loss function is introduced to solve the contradiction of the CIOU loss function used in the original YOLOv8. Experimental results demonstrate that the proposed HCDFI-YOLOv8 model achieves a 2.7% improvement in mAP@0.5 and a 2.5% improvement in mAP@0.5:0.95 compared to standard YOLOv8. Among twelve models for icing detection, the proposed model delivers the highest overall detection accuracy. The accuracy of the HCDFI-YOLOv8 model in detecting complex transmission line environments is verified and effective technical support is provided for transmission line ice cover detection. Full article
(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems—2nd Edition)
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