AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using Side-Scan Sonar Imagery
“Ocean conservationists use underwater acoustic sensors to scan the seafloor for lost fishing nets and debris, but reviewing thousands of hours of data manually is extremely slow. We are building an AI software system that automatically scans these sonar images, ignores natural rock formations, and pinpoints exact locations of man-made underwater hazards.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An end-to-end computer vision software platform featuring an AI object detection engine, acoustic noise filter, geotagging parser, and an interactive GIS dashboard.
WHO IS IT FOR?
Problem faced by marine conservationists, NIOT researchers, and ocean cleanup crews; Solution used by drone operators, marine surveyors, and environmental monitoring agencies.
WHY DOES IT EXIST?
The ocean is vast and dark, making visual cameras useless. Side-scan sonar produces complex acoustic shadow imagery that is heavily prone to speckle noise, causing human operators to miss deadly ghost nets hidden among natural rocks and ridges.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Raw side-scan sonar image logs (XTF, SDF, or TIFF formats), associated ping navigation metadata files (CSV/TXT with latitude, longitude, and depth headers).
Acoustic noise suppression and contrast enhancement, deep learning inference using customized YOLO or U-Net models for anomaly detection, shadow-to-object spatial validation, and coordinate interpolation.
An interactive web dashboard displaying bounding boxes and segmentation masks over sonar maps, structured JSON/CSV anomaly reports with precise GPS coordinates, dimensions, and confidence scores.
CONSTRAINTS
- Must handle high speckle noise and acoustic shadows without massive false positive spikes
- Must be lightweight enough to run locally on edge devices or onboard marine drones
- Must correctly parse variable-resolution sonar data and navigation header logs
- Must operate completely offline without heavy cloud computing dependencies
JURY METRICS
- Mean Average Precision (mAP) for debris and shipwreck identification
- False Alarm Rate (FAR) reduction against natural rock clusters and sand ripples
- Inference latency per sonar waterfall frame (frames per second on edge hardware)
- UI responsiveness and accuracy of geotagged export reports
CORE BOTTLENECK
Distinguishing true artificial anomalies (like ghost nets or pipes) from natural acoustic shadow artifacts cast by irregular rock formations and underwater ridges.
Why You Should Choose This PS (Pros)
- High social and environmental impact aligning directly with UN Sustainability Goals and MoES priorities
- Clear, modular scope with well-defined inputs and outputs (AI model + Dashboard + Report generator)
- Strong visual appeal ('wow factor') during the live demo via interactive sonar mapping and bounding boxes
Challenges & Risks to Watch (Cons)
- Lack of standardized, publicly available annotated side-scan sonar datasets for ghost nets
- Complex physics of acoustic imagery (shadow inversion, slant-range distortion) requires domain understanding
- Crowded technical domain if teams rely purely on off-the-shelf standard object detection without sonar-specific tuning
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Because the ocean is vast and dark, marine conservationists and underwater technologists rely on Side Scan Sonar (SSS) instruments. These sensors are towed behind ships or mounted on Autonomous Underwater Vehicles (AUVs) to create detailed acoustic maps of the seafloor.However, manual inspection of thousands of kilometers of sonar logs is incredibly slow, tedious, and prone to human error. Debris can easily blend into natural geological features like rock formations, sand ripples, and marine ridges. Automating this process via computer vision is essential for efficient ocean cleanup operations.