Design of a Real-Time Monitoring and Early Warning System for Engineering Safety Hazards Using Image Analysis Technology

As the scale of engineering projects continues to grow, safety management on construction sites faces significant challenges. Traditional methods such as manual inspections and periodic checks struggle to achieve real-time and effective monitoring of potential hazards, which can lead to accidents. In recent years, image analysis technology has increasingly been applied to the monitoring of engineering safety hazards due to its automation, Areal-time capabilities, and high efficiency. However, existing image analysis algorithms still encounter issues such as insufficient tracking accuracy and delayed warning responses in complex engineering environments. To address these problems, this study proposes aArealtime hazard tracking and identification method based on an improved Mean Shift algorithm, combined with a support vector machine (SVM) for critical state early warning of engineering safety hazards. The system improves recognition accuracy and early warning response speed in complex environments through algorithm optimization, offering higher practicality and reliability. This provides a technical safeguard for safety management at construction sites.