How AeroResQ™ identifies distress in under five seconds

By Anh Phan

21 November 2025

AIAutonomous Distress Detection

Every summer, our operations team reviews incident logs from beaches around the world. A pattern always emerges: by the time a distressed swimmer is noticed from shore, precious seconds have already slipped away. AeroResQ™ was designed to change that, seeing early signals of danger faster than the human eye, filtering noise from real risk, and informing lifeguards clearly without ever overwhelming them.

The Problem: Early Distress Doesn’t Look Dramatic

Most drowning victims don’t wave. They don’t shout. They don’t thrash. They slip underwater quietly.

Operational research from the International Life Saving Federation shows that up to 85% of drowning episodes begin with subtle posture changes: vertical body alignment, reduced kicking, delayed arm recovery, or forward bobbing. These signs are easy for humans to miss, especially when watching hundreds of swimmers in glare and surf.

This is why AeroResQ™ is built to observe swimmers continuously, analyze motion patterns, and alert lifeguards the moment behavior becomes abnormal, not after a visible struggle.

A Multi-Sensor Approach to Precision

To detect distress early, AeroResQ™ fuses inputs from three synchronized sensors:

• Thermal Imaging: Highlights heat signatures during low light, fog, or glare, maintaining visibility.
• RGB Polarized Camera: Cuts through surface reflections and wave glare to reveal silhouettes invisible to standard cameras.

All vision channel are processed on an embedded NVIDIA module running custom AI models optimized specifically for water-rescue environments.

The AI Pipeline: What Happens in Those Five Seconds

🟢 Step 1 — Detection (0.0s to 0.2s)

A convolutional detector identifies all visible swimmers, bounding each one and tracking their position frame-to-frame.

🟠 Step 2 — Behavior Analysis (0.2s to 2.5s)

A transformer-based motion model compares each swimmer’s pattern against thousands of archived rescue scenarios, identifying:

• fatigue drift
• vertical posture collapse
• “climbing the ladder” instinctive drowning response
• irregular breathing or arm recovery

The system filters out harmless actions like diving, splashing, or playing.

🔴 Step 3 — Risk Classification (2.5s to 4.0s)

• Swimming (Green): Normal stroke, stable rhythm.
• Risk (Orange): Early fatigue, slowed movement, drift.
• ResQ Needed (Red): Loss of buoyancy, vertical posture, repeated bobbing, or sudden submersion.

⚪ Step 4 — Human-in-the-Loop Verification (4.0s to 5.0s)

Once AeroResQ™ is confident a swimmer may be in danger, the system sends the event summary such as bounding boxes, motion traces, and confidence scores, to the lifeguard for rapid human confirmation. The AI never launches a rescue on its own; it elevates only high-confidence cases, and the lifeguard makes the final call.

Three Pillars Behind AeroResQ™ Speed and Reliability

• Layered Sensing: Designed to see through glare, fog, and choppy water.
• Explainable AI: Alerts include motion graphs, bounding boxes, confidence scores, and recommended actions.
• Human Oversight: AeroResQ™ supports lifeguards, it never replaces them.

Results From Field Pilots

• 63% reduction in false alarms after adding verification layers
• 98% accuracy in identifying genuine distress events
• Alerts delivered up to minutes earlier than tower-based scanning
• Lifeguards reported greater confidence and reduced fatigue

The biggest takeaway: Speed + clarity saves lives.

What Happens Inside the Five-Second Pipeline

• Detection (0–0.2s): Swimmers are identified and tracked in real time.
• Behavior Analysis (0.2–2.5s): Motion models compare swimmer movement to thousands of archived rescue scenarios.
• Risk Classification (2.5–4.0s): Each swimmer is labeled as Swimming (green), Risk (orange), or ResQ Needed (red).
• Human Verification (4.0–5.0s): A module filters out false alarms before notifying lifeguards.

What’s Coming Next

• Long-range optical zoom for deep-water detection
• Improved night-vision fusion
• Predictive drift modeling using ocean current data
• Automated drone deployment from shore towers

Each update brings us closer to a world where every beach has an extra set of eyes, eyes that never blink.

During coastal trials, AeroResQ™ cut false alarms by 63% and improved confirmed distress detection to 98%. Lifeguards reported greater confidence and reduced scanning fatigue, especially during peak hours.