YOLOv9-Enabled Vehicle Detection for Urban Security and Forensics Applications
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The integration of artificial intelligence (AI) techniques in vehicle detection holds significant promise, particularly in forensic and security domains. Leveraging object detection algorithms enables real-time monitoring of vehicles by competent authorities, aiding in continuous surveillance of roads and highways for various surveillance objectives. Additionally, it streamlines tasks such as identifying stolen vehicles, tracking suspects, and enforcing traffic regulations. Object detection technology also proves invaluable in forensic analysis, aiding criminal investigations and accident reconstructions. Furthermore, it enhances security by detecting aberrant behavior and potential threats at critical infrastructure sites. Concurrently, the remarkable advancements in unmanned aerial vehicles (UAVs) have led to their widespread application across diverse domains, including traffic monitoring and intelligent transportation systems. Equipped with high-resolution cameras, UAVs offer precise imagery for vehicle detection, facilitating swift responses to incidents. This study focuses on vehicle detection from aerial urban transportation images using YOLOv9 on a UAV platform, demonstrating the feasibility and efficacy of aerial analysis for efficient vehicle detection and timely alerts to competent authorities. © 2024 IEEE.
Açıklama
12th International Symposium on Digital Forensics and Security, ISDFS 2024 -- 29 April 2024 through 30 April 2024 -- San Antonio -- 199532
Anahtar Kelimeler
aerial monitoring, forensic investigation, UAV, vehicle detection, YOLOv9
Kaynak
12th International Symposium on Digital Forensics and Security, ISDFS 2024
WoS Q Değeri
Scopus Q Değeri
N/A
