Project Background
Qibaiyang Forest Park, located in the Nancheng Development Zone of Lishui City, Zhejiang Province, is rich in natural landscapes and ecological resources.
In recent years, both natural and human-caused forest fires have become more frequent, posing serious threats to the park’s ecological safety and the security of visitors.
To address these challenges, an advanced forest fire prevention monitoring system was developed to enhance early detection and rapid response capabilities.
Project Objectives & Requirements
The primary goal of the project is to prevent forest fires through real-time monitoring and immediate risk warnings.
The system needed to reliably detect smoke and fire signals, identify unauthorized intruders, and monitor visitor density to ensure overall safety of the forest ecosystem.
Technology Solution & Implementation
The project integrates three core AI-powered technologies:
Forest Fire & Smoke Detection
Using advanced video analysis, the system detects early-stage fire indicators by analyzing smoke patterns and flame characteristics from surveillance footage.
Intrusion Detection
By analyzing human body features and movement patterns, the system identifies unauthorized personnel entering restricted forest zones.
People Counting & Crowd Density Monitoring
This module tracks the number of visitors in designated areas to prevent safety incidents caused by overcrowding and to support crowd management.
Project Results & Benefits
Since deployment, the monitoring system has successfully prevented multiple potential fire incidents and promptly detected several unauthorized intrusions.
This solution has significantly enhanced the park’s safety management capabilities and provided robust protection for both visitors and forest resources.
Challenges & Future Outlook
One of the main challenges during implementation was improving algorithm accuracy and response speed under complex forest environments.
Looking ahead, the project aims to incorporate more advanced AI technologies—such as deeper neural networks and multi-sensor fusion—to further enhance system performance.
Additional functions, such as automated alert escalation and more intelligent decision-support tools, will also be introduced to create a more comprehensive and fully intelligent forest monitoring system.