| Title: |
Research Progress of Event Intelligent Perception Based on DAS |
| Authors: |
Di Wu; Qing-Quan Liang; Bing-Xuan Hu; Ze-Ting Zhang; Xue-Feng Wang; Jia-Jun Jiang; Gao-Wei Yi; Hong-Yao Zeng; Jin-Yuan Hu; Yang Yu; Zhen-Rong Zhang |
| Source: |
Sensors ; Volume 25 ; Issue 16 ; Pages: 5052 |
| Publisher Information: |
Multidisciplinary Digital Publishing Institute |
| Publication Year: |
2025 |
| Collection: |
MDPI Open Access Publishing |
| Subject Terms: |
distributed acoustic sensing (DAS); event intelligent perception; machine learning; deep learning |
| Description: |
This review systematically examines intelligent event perception in distributed acoustic sensing (DAS) systems. Beginning with the elucidation of the DAS principles, system architectures, and core performance metrics, it establishes a comprehensive theoretical framework for evaluation. This study subsequently delineates methodological innovations in both traditional machine learning and deep learning approaches for event perception, accompanied by performance optimization strategies. Particular emphasis was placed on advances in hybrid architectures and intelligent sensing strategies that achieve an optimal balance between computational efficiency and detection accuracy. Representative applications spanning traffic monitoring, perimeter security, infrastructure inspection, and seismic early warning systems demonstrate the cross-domain adaptability of the technology. Finally, this review addresses critical challenges, including data scarcity and environmental noise interference, while outlining future research directions. This work provides a systematic reference for advancing both the theoretical and applied aspects of DAS technology, while highlighting its transformative potential in the development of smart cities. |
| Document Type: |
text |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Optical Sensors; https://dx.doi.org/10.3390/s25165052 |
| DOI: |
10.3390/s25165052 |
| Availability: |
https://doi.org/10.3390/s25165052 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.151F356F |
| Database: |
BASE |