How I Built an AI-Powered Drowning Detection System

As an engineer, I’ve always been concerned about pool safety. Traditional pool alarms are noisy and prone to false alarms, and they can’t distinguish between a child playing and a child in distress. I knew I could build something better. I created an AI-powered drowning detection system that uses computer vision to provide real-time, intelligent alerts. It’s a project I’m incredibly proud of, and it achieves 95% accuracy with a sub-500ms alert time.

The Problem I Was Solving

Let me be honest: existing pool safety systems are terrible. I researched every commercial option on the market, and they all had the same fundamental flaw. They relied on simple motion sensors or pressure plates that couldn’t differentiate between normal swimming behavior and an actual emergency. The result? False alarms. Lots of them.

I spoke with several families who had installed these systems, and the story was always the same. The first few times the alarm went off, everyone rushed to the pool in a panic. By the tenth false alarm, people started questioning whether the system was worth the hassle. By the twentieth, they’d either disabled it or learned to ignore it completely. This is what experts call “alert fatigue,” and it’s dangerous because when a real emergency happens, no one responds with the urgency they should.

My goal was different. I wanted to build a system that understood context, that could watch the pool like an experienced lifeguard would, recognizing the subtle differences between normal play and genuine distress. The system needed to be smart enough to earn trust through reliability.

The Solution I Built: A Hybrid Edge-Cloud System

I spent a lot of time thinking about the architecture before I wrote a single line of code. The key insight was that I needed two things that don’t usually go together: lightning-fast response times and the ability to continuously learn and improve. That’s where the hybrid approach came in.

Here’s what I built:

The Edge Layer (Where Speed Matters)

I started with the edge infrastructure because response time was critical. Every millisecond counts in a drowning situation. I chose the NVIDIA Jetson Nano as my edge compute platform. It’s small enough to mount near the pool equipment, has enough GPU power to run computer vision models in real-time, and costs less than $100. I connected it to existing CCTV cameras that most homes with pools already have installed.

The edge device runs a highly optimized TensorFlow Lite model that I trained to recognize drowning patterns. It processes the video feed at 30 frames per second, analyzing body positions, movement patterns, and time spent underwater. When it detects something that matches a distress pattern, it triggers an immediate local alarm. No internet required. No cloud latency. Just instant response.

This edge-first design was non-negotiable for me. Internet connections go down. Cloud services have outages. But if a child is in distress, the alarm needs to sound regardless of what’s happening with the WiFi.

The Cloud Layer (Where Intelligence Grows)

While the edge handles the critical real-time detection, the cloud is where the system gets smarter over time. I set up a Kubernetes cluster on GKE running Ray for distributed model training. The edge devices don’t just run the models, they also act as data collectors. They upload interesting clips back to the cloud: near-misses, unusual swimming patterns, times when someone jumped in unexpectedly, anything that might help improve the model.

I built an automated retraining pipeline that runs every week. It takes all the new data, combines it with the existing training set, and trains an improved model. If the new model performs better in validation (which I test against a carefully curated dataset of real drowning incidents captured from public safety videos), it gets automatically deployed to all the edge devices. The whole thing runs without me touching it.

The Features That Made It Work

Pattern Recognition That Actually Understands Swimming

The core of the system is its ability to analyze swimming patterns, not just detect motion. I trained the model on thousands of hours of swimming footage, both normal activity and emergency situations. It looks at body orientation, the rhythm of movements, time spent underwater, and whether someone is making forward progress or just thrashing in place. A kid doing an underwater handstand looks completely different from someone in distress, even though both involve being underwater for extended periods.

Context-Aware Sensitivity

This is the feature that took the most work but made the biggest difference. The system uses facial recognition to identify different people and adjusts its sensitivity accordingly. When it recognizes a child, it watches more carefully and has a lower threshold for triggering alerts. With adults, it gives more leeway because they’re typically stronger swimmers. This dramatically reduced false positives while maintaining high detection rates for the people who need it most.

Resilient by Design

I built redundancy into every layer. The edge device has its own battery backup and can run for 12 hours without power. All the critical detection logic runs locally, so internet outages don’t matter. The system logs every decision it makes, which helped me debug issues during testing and now provides accountability.

What I Actually Achieved

After three months of development and another month of testing, here’s where the system landed:

The latency from incident detection to alarm activation is under 500 milliseconds. I tested this hundreds of times with simulated drowning scenarios using a mannequin. The system consistently beat my reaction time when I was watching the same footage.

Accuracy was my biggest concern going into this project. I validated the system against a test set of 200 real drowning incidents (sourced from public safety training videos) and 2,000 hours of normal swimming footage. It caught 95% of the real incidents and had a false positive rate of less than 2%. That’s roughly one false alarm per month for a pool that gets used every day.

The reliability exceeded my expectations. The edge-first architecture delivers 99.5% uptime for the core detection, even when the internet connection is flaky. I’ve had it running continuously for six months now, and it’s only gone offline twice, both times due to power outages that also knocked out the cameras.

Cost was another win. By doing the heavy processing at the edge, I cut cloud compute costs by more than 60% compared to a cloud-only design that would need to stream and analyze video 24/7. The entire system costs less than $200 in hardware plus maybe $10 a month in cloud costs for model training.

What This Means

Building this system taught me that the best AI solutions aren’t about throwing the biggest models at a problem. They’re about understanding the constraints deeply and architecting around them. Speed mattered more than accuracy above 95%. Reliability mattered more than cutting-edge features. Local processing mattered more than cloud scalability.

The system has been running in real deployments for several months now. No real emergencies yet, thankfully, but I’ve seen it catch simulated incidents during tests that I missed when watching the same footage. That’s the goal: a system that watches with the attention and expertise that humans can’t sustain 24/7.

This drowning detection system is part of my broader work in production MLOps: