Core Technology

AI-Powered Detection

ResQon employs cutting-edge deep learning algorithms to detect human presence in challenging fire emergency environments. Our proprietary computer vision technology can identify survivors through smoke, limited visibility, and complex structural layouts, providing life-saving information to rescue teams.

The system uses a combination of thermal imaging, RGB cameras, and advanced neural networks to process visual data in real-time, detecting human shapes and movements even in conditions where traditional visual identification would fail.

Key Technologies

Our AI detection system integrates multiple advanced technologies to achieve reliable human detection in emergency scenarios. Each component plays a crucial role in the system's ability to identify survivors in challenging environments.

Convolutional Neural Networks

Our proprietary CNN architecture is specifically trained on emergency scenario data, enabling the system to recognize human shapes and movements even when partially obscured by smoke or debris. The multi-layer neural network can identify subtle patterns invisible to the human eye.

Thermal Imaging Analysis

Advanced thermal sensors detect heat signatures through smoke and walls, while specialized algorithms distinguish between human heat patterns and background thermal noise from the fire environment. This allows for detection even when visual confirmation is impossible.

Decision Tree Algorithms

Multi-parameter decision trees analyze sensor data in real-time to classify objects with high confidence, reducing false positives in complex environments. These algorithms help filter out non-human heat sources and movement patterns during emergency situations.

Real-time Data Processing

Edge computing architecture allows for millisecond-level processing of sensor inputs directly on the drone, ensuring life-critical detection information is available without network delays or connectivity issues common in emergency scenarios.

Confidence Mapping

Our system generates confidence maps showing probability distributions of human presence across the environment, enabling rescue teams to prioritize high-confidence areas while still being aware of potential locations requiring further investigation.

Continuous Learning

The system improves with each deployment through our secure federated learning framework. Detection algorithms are continuously enhanced based on real-world performance data while maintaining strict privacy and security protocols.

Detection Performance

ResQon's AI detection systems have been rigorously tested in real-world fire scenarios and controlled training environments. The results demonstrate exceptional detection capabilities even in challenging conditions.

Smoke Penetration Testing

In controlled tests with dense smoke conditions (visibility less than 1 meter), ResQon achieved a 94% detection rate for human subjects at distances up to 15 meters, significantly outperforming traditional visual detection methods.

View test results

Multi-Floor Detection

During simulated multi-floor building fire exercises, the system successfully mapped survivor locations across complex layouts, providing accurate position data within 1.2 meters in 98% of test scenarios.