Coast Scan

Smart India Hackathon 2025 National Winner (Hardware Edition)

Problem Context

Coastal surveillance and monitoring are critical for national security, yet traditional methods rely heavily on manual patrolling or expensive satellite imagery which lacks real-time granularity.

Why Existing Solutions Were Insufficient

Existing automated systems are often prohibitively expensive or struggle with the harsh environmental conditions of coastal areas. Manual monitoring is labor-intensive and prone to human error.

Constraints

Cost: Must be affordable for widespread deployment.
Connectivity: Needs to function in low-network zones.
Durability: Hardware must withstand humidity and saline environments.

System Architecture

The system utilizes a network of IoT sensors and cameras connected to a central edge processing unit. Data is processed locally to detect anomalies (like unrecognized vessels or distress signals) before being transmitted to the command center.

Tech Stack

Python Deep Learning (YOLO) IoT Frameworks Raspberry Pi

Validation & Results

Prototype successfully laid out at the SIH 2025 finals, demonstrating real-time object detection with 92% accuracy under simulated weather conditions.

Outcome

Secured the National Winner title at SIH 2025, validating the approach of using low-cost hardware for high-stakes national security applications.