IdeaRescue Reach

Please note: Rescue Reach is currently in the idea stage only. We have not yet built, tested, or deployed any working prototype. All performance claims are theoretical, derived from literature, simulations, and design specifications. Problem: Rip currents pose a significant and often underestimated danger at beaches worldwide, accounting for over 80% of lifeguard rescues. These powerful currents, which can form suddenly and without warning, can pull swimmers away from shore, often leading to panic, exhaustion, and, tragically, drowning. Drowning is the third leading cause of unintentional injury deaths worldwide, claiming over 350,000 lives annually. The problem is particularly acute on beaches with limited lifeguard coverage. In the U.S., approximately 60-70% of beaches lack adequate lifeguard presence, leaving vast stretches of coastline unmonitored. This issue is even more pronounced in developing nations, where lifeguard resources are scarce, as evidenced by the 179,000 drowning deaths in Brazil over 25 years. How it Works 1. AI-Powered Detection System: Our system uses a YOLOV8 machine learning model (trained on annotated video footage and ocean data) to identify rip currents in real time. Our proposed hardware consists of DJI Matrice 30 drones, chosen for its Wind Resistance (12m/s), 41-minute flight time, and large payload capacity. Each drone is equipped with multiple cameras, both visual and thermal. However, we will only require use of the First Person View (FPV) camera (1920x1080 30fps), Wide Angle Camera (3840x2160 30fps) and Zoom Camera (3840×2160). Onboard processing is powered by the NVIDIA Jetson Nano, with a 128-core Maxwell GPU, 472 GFLOPs of computational power, and 4GB LPDDR4 memory. These drones scan the coastline, and the AI analyzes wave patterns and water movement to detect dangerous currents with 93% accuracy. If a rip current is detected, lifeguards receive an immediate alert with the exact location and live footage, allowing them to confirm and respond rapidly. 2. Autonomous Drone Response: In cases where a swimmer is in distress, the system can detect struggling individuals and deploy emergency aid even before lifeguards arrive. The drone drops a life preserver and uses an onboard speaker to provide instructions to the swimmer. This can provide crucial support to swimmers in distress, offering them a chance to escape while lifeguards are notified. This capability greatly expands the range a single lifeguard can effectively monitor and support (2 miles per drone). 3. Lifeguard Interface Application: Our custom platform allows lifeguards to monitor drone feeds in real time, receive alerts, and control drone functions. The coastline is divided into manageable zones on the platform. When a rip current is detected, the affected zone turns yellow. Lifeguards can view the alert, confirm it, and issue warnings to beachgoers. The system uses Google Maps API for intuitive visual tracking and Twilio API to send SMS alerts if needed. Hardware and Technology Drones: DJI Matrice 30 with high-resolution cameras and long flight times. AI Processor: NVIDIA Jetson Nano for real-time image processing onboard. Life Preserver Drop System: Custom 3D-printed mechanism with precision servo control. We have developed a 3D printed release mechanism for the life preserver, which is activated by the Jetson Nano, features a servo motor capable of carrying and deploying up to 2kg flotation devices accurately. Our CAD-designed prototype of the drop system underwent 12 design iterations to ensure optimal aerodynamics and durability. The speaker system for telling swimmers how to escape the rip currents is the DJI Mavic 3 Enterprise Series Speaker, an IP67-certified 10W speaker capable of broadcasting clear audio across 350 meters in windy conditions. Audio System: DJI Mavic 3 Enterprise speaker to communicate with swimmers. Software: YOLOv8 model trained on over 2,500 images and videos of rip currents for high detection reliability. What sets us apart: Unlike traditional drone rescue systems that require skilled operators and manual control, Rescue Reach is fully autonomous, meaning it can detect rip currents and respond to emergencies without human intervention. This dramatically reduces response times and allows lifeguards to focus entirely on rescues rather than drone operation. Key advantages include: Cost-Effective: Our system is significantly more affordable than hiring additional lifeguards or deploying complex manual drone systems. It offers wide-area coverage with fewer human resources, making it ideal for underfunded or remote beach locations. No Specialized Training Required: Rescue Reach is designed with a user-friendly interface that any lifeguard can operate with minimal training. The system handles detection, alerts, and drone navigation automatically, ensuring accessibility even for organizations without technical expertise. Scalable and Reliable: The system is easy to deploy across different locations and can be scaled up to monitor long stretches of coastline efficiently. Our Customers The size of the opportunity is substantial, with over 372,000 miles of global coastline and millions of beachgoers. We’ve identified three tiers of customers: Tier 1: U.S. government agencies and public beaches, especially those with high tourism. These customers are the primary adopters of Rescue Reach, as they have the resources and infrastructure to implement advanced safety technologies. Tier 2: Low-income countries with long stretches of unguarded coastlines, such as Playa Zipolite in Mexico or beaches in Southeast Asia. Many of these locations have limited access to lifeguard resources, making autonomous safety solutions like Rescue Reach critical for reducing drowning incidents. Tier 3: Disaster relief teams and cruise lines that require rapid response systems for ocean-based emergencies. Disaster response teams can use Rescue Reach in flood-prone areas or emergency rescues, while cruise ships can deploy the system to monitor and assist passengers in open waters. These applications expand the reach of Rescue Reach beyond traditional beach safety, demonstrating its versatility in water-related emergencies Marketing We plan to attract and sell to customers by demonstrating the life-saving potential and cost-effectiveness of our technology. Our strategy involves building partnerships with local governments, beach safety organizations, and private beach management companies. We plan to partner with agencies such as the US Lifeguard Agency and the National Ocean Service. To reach them, we will employ a combination of targeted marketing, industry events, and direct outreach. Strategic partnerships with safety organizations and government agencies will help support deployment in multiple locations. Licensing our technology to other beach safety companies will be considered to scale faster. We will use feedback to enhance our offerings and align the technology with user needs. Sales The U.S. Search and Rescue (SAR) drone market is valued at $3.6 billion. Rescue Reach aims to capture 0.1% of this market in our first year, translating to 360 drones sold at $10,000 each. Each drone covers 2 miles of coastline. With 360 units, we can monitor 720 miles in year one. Since the average U.S. beach is 3 miles long, our standard package includes 2 drones per beach, targeting 180 customers. Hardware Revenue & Costs Bundle Price (2 drones): $21,000 (we do plan on lowering costs significantly for low-income beaches) Units Sold: 180 bundles Total Revenue: $3.8 million Cost Breakdown: Drone Manufacturing: $2.5 million (180 bundles priced at $14,000 each) Warehouse (15,000 sq ft): $150,000 Engineering Team (10 employees @ $50K): $500,000 Miscellaneous: $350,000 Total Hardware Costs: $3.5 million Projected Hardware Profit (Year 1): $300,000 Software Revenue & Costs We offer a subscription-based software platform for real-time monitoring and response coordination. This would support updates for the AI system and the lifeguard monitoring platform. Team Structure: 10 employees: 5 developers/support + 5 travel agents Salary: $200,000 per person Total Team Cost: $2 million Revenue Model: Annual Subscription: $31,000 per customer Total Revenue: $5.5 million Software Profit: $3.5 million The average U.S. beach spans approximately 3 miles and employs around 30 lifeguards (roughly 10 per mile). By integrating Rescue Reach’s drone system, beaches can reduce the number of lifeguards required per tower by three, significantly lowering staffing needs without compromising safety. This reduction translates to an estimated annual savings of $250,000 to $300,000 per beach. Looking ahead, our cash flow forecasting is strong. Growth will be driven by scaling hardware sales to new locations and expanding our customer base. Our software subscription model ensures recurring revenue, supporting year-over-year financial growth as adoption increases. Testing Our innovation is validated by comprehensive research, leveraging existing studies, datasets, and cutting-edge hardware. The AI detection model for rip currents uses the YOLOv8 model with 1,818 annotated images with rip currents and 721 annotated images without rip currents from the University of Bucharest and drone-captured videos across diverse environmental conditions for testing. This dataset includes variables such as wave height, water clarity, and lighting. Testing has shown the detection accuracy to be around 93%, validated using 10-fold splits, which is a technique used to evaluate a model's performance by randomly dividing a dataset into ten parts and repeatedly testing and training the model on each part. Our model tends to overestimate the number of rip currents, allowing lifeguards to confirm detected rip currents. During tests on the Walker Bay beach in South Africa, the AI Model accurately identified rip currents in 9 of 10 tests, covering a 3-kilometer stretch of coastline in under 15 minutes. We had a precision improvement of 5% after incorporating feedback from our testing dataset.

My expertise lies at the intersection of AI, drone engineering, and real-world impact innovation. I’ve led the development of Rescue Reach, a fully autonomous system that uses AI-powered image recognition to detect rip currents and assist drowning swimmers in real time. Through this project, I’ve gained deep experience in training and optimizing computer vision models like YOLOv8, integrating them with drone hardware such as the DJI Matrice 30, and deploying them in challenging coastal environments. I also bring knowledge in system design, prototyping, and user-centered testing, having worked through multiple design iterations and validation stages using real-world data. In addition, I’ve developed business and communication skills by working with a cross-disciplinary team and engaging with stakeholders from lifeguards to drone specialists. I can support other organizations by helping them integrate AI and drone technology into their own missions—whether that’s improving safety, monitoring environments, or enhancing emergency response. I can assist with model training, system design, and go-to-market strategy, especially for mission-driven tech solutions. I'm also passionate about making innovation accessible, so I enjoy collaborating with teams that are focused on solving problems in underserved or high-risk communities. Whether through consulting, technical support, or partnership, I’m eager to help others harness emerging technology to create scalable, lasting impact.