Abstract
Urban expansion and complex environments are escalating security challenges. This research presents a drone-based risk assessment approach, significantly enhanced by the YOLOv8 algorithm. Through continuous iterations, the algorithm's performance has improved approximately fourfold, greatly boosting detection accuracy for low-altitude law enforcement and overcoming the constraints of existing surveillance technologies. Furthermore, the algorithm was applied to five major marathon events, conducting security risk assessments based on EWM-TOPSIS and FCM clustering. The final risk ranking of the events was Zhanjiang, Shanghai, Qingdao, Jiangsu, and Tianjin, with the Zhanjiang Marathon showing a people risk index of 72.12, indicating a need for strengthened security measures. This research applies artificial intelligence to public safety, delivering a scalable solution for a variety of security challenges.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
