Perimeter Security Utilizing Thermal Object Detection

Area of Science:

• Computer Science
• Engineering
• Security Technology

Background:

• Artificial intelligence (AI) is increasingly applied across diverse fields, including security and surveillance.
• Automatic surveillance systems are becoming more prevalent, driving demand for advanced technologies.
• Thermal detection offers advantages like 24-hour operation and robustness against environmental changes.

Purpose of the Study:

• To evaluate the effectiveness of thermal automatic detection systems for surveillance.
• To assess the role of AI object detection models in the thermal spectrum for security.
• To determine the suitability of thermal imaging for reliable and efficient threat detection.

Main Methods:

• Examining object detection models operating exclusively in the thermal/infrared spectrum.
• Evaluating system performance based on detection capabilities and efficiency.
• Assessing robustness against varying illumination and weather conditions.

Main Results:

• Thermal detection provides continuous, uninterrupted surveillance capabilities.
• AI models in the thermal spectrum show potential for efficient threat identification.
• The technology demonstrates resilience to environmental and illumination challenges.

Conclusions:

• Thermal automatic detection systems are a viable component for advanced surveillance.
• AI-powered thermal imaging enhances the reliability and efficiency of security mechanisms.
• Further evaluation of thermal object detection models is recommended for surveillance applications.