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Advanced Sensing and Imaging Devices for Detection, Tracking, Monitoring and Reconstruction

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2549

Special Issue Editors


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Guest Editor
Spatial Artificial Intelligence Lab (SAIL), School of Electronics and Communication Engineering, Sun Yat-sen University (Shenzhen Campus), Shenzhen 518107, China
Interests: human-centered visual content understanding and reasoning: this area seeks to enable machines to actively perceive, analyze, and interpret human states, behaviors, and underlying motivations in dynamic scenarios; omni-modal scene perception and navigation: this emphasizes harnessing diverse sensor modalities to comprehend and navigate complex scenes; machine behavior planning and decision-making: this direction is centered on equipping intelligent agents with the ability to make real-time decisions based on their comprehension of understanding surroundings

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Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: AI generated content (AIGC); multimodality sensing; computer vision and pattern recognition

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore cutting-edge advancements in sensing and imaging technologies that enhance detection, tracking, monitoring, and reconstruction across various applications. From environmental surveillance to autonomous systems and industrial inspection, innovative sensors and imaging systems are revolutionizing data acquisition and analysis. We seek high-quality research on novel device designs, computational imaging, AI-enhanced sensing, multi-modal fusion, and real-time processing techniques that push the boundaries of accuracy, efficiency, and scalability.

Dr. Ruimao Zhang
Dr. Yukai Shi
Guest Editors

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Keywords

  • smart multi-sensor networks
  • computational imaging for sensor fusion
  • multi-sensor object detection and tracking
  • next-generation multi-modal sensing
  • real-time sensor-based monitoring
  • 3D reconstruction with multi-sensors
  • advanced remote sensing technologies
  • autonomous surveillance systems
  • AI-driven sensor fusion
  • emerging sensor architectures and design

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Published Papers (1 paper)

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Research

27 pages, 14341 KB  
Article
UAV and Deep Learning for Automated Detection and Visualization of Façade Defects in Existing Residential Buildings
by Yue Fan, Jinghua Mai, Fei Xue, Stephen Siu Yu Lau, San Jiang, Yiqi Tao, Xiaoxing Zhang and Wing Chi Tsang
Sensors 2025, 25(23), 7118; https://doi.org/10.3390/s25237118 - 21 Nov 2025
Cited by 2 | Viewed by 2065
Abstract
As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission [...] Read more.
As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures. Full article
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