SIH26057/Disaster Management

Sonar marine debris detection system

An AI system that processes underwater sonar images to automatically detect man-made debris and output a location report for marine operators.

Ministry of Earth Sciences (MoES) · National Institute of Ocean Technology (NIOT) · Software

Teams already in: Not read yet
Buildable in 36 hours: ModerateOur estimate, from their wording
Hard blockers: 1Restricted data
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

Why this fits you

This organisation declared winners on 9 problem statement(s) in SIH 2025 and on 6 in SIH 2024. It has awarded 2 joint winner(s), so it sometimes splits a prize between two teams.

What argues against it

Potential concern: needs data you may not get.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Object Detection Model
An artificial intelligence model trained to locate man-made debris and structures in sonar images.
02
Noise Filtering Module
A pre-processing filter that removes false positives from natural rock clusters and acoustic shadows.
03
Reporting and Geotagging Engine
A script that reads sonar metadata and outputs structured reports in JSON or CSV format with location details.
04
UI Dashboard
A visual interface where users can upload sonar logs, view detections overlaid on a map, and download reports.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The evaluators will check if your AI model can handle high speckle noise and acoustic shadows, successfully filter out natural rock formations, output confidence scores, and generate structured location reports with a working user interface dashboard.

They never say

labeled side-scan sonar training dataset
exact sonar metadata format or coordinate file structure

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Labeled side-scan sonar training dataset?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact sonar metadata format or coordinate file structure?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

8 more— the ones a panel gets to if there is time

The brief asks for object detection model. How would you build that?
Decoded from SIH26057 itself — An artificial intelligence model trained to locate man-made debris and structures in sonar images. The brief asks for it by name.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

Needs data you may not get
The text requires training an AI model on side-scan sonar imagery to detect specific debris, but the organisers are not providing a dataset.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Nothing in the text says it depends on a dataset we would have to find.

Also from Ministry of Earth Sciences (MoES)

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26057, set by Ministry of Earth Sciences (MoES).
Its official title, word for word: "AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using Side-Scan Sonar Imagery"
Theme: Disaster Management. Category: software.

In plain terms: An AI system that processes underwater sonar images to automatically detect man-made debris and output a location report for marine operators.

## The organisers' full description, verbatim

• Background The accumulation of anthropogenic (man-made) debris in marine ecosystems poses a critical threat to global biodiversity. Among the most destructive types of pollution are â€˜ghost netsâ€™â€”abandoned, lost, or discarded fishing gear. These nets continuously trap and kill marine life,destroy coral reefs, and damage commercial vessel propellers. 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. • Description Participants must develop an end-to-end automated computer vision pipeline capable of ingesting side-scan sonar imagery, identifying man-made debris against a complex natural background, and generating actionable localized data.The software system must be robust enough to handle the core challenges inherent to acoustic imagery: high speckle noise, varying pixel resolutions, acoustic shadows, and data dropouts caused by underwater vehicle motion (heave, pitch, and roll). The primary objective is to build an algorithm that reliably separates natural seafloor topology from artificial anomalies. The final solution should be optimized to run efficiently, potentially allowing deployment on edge devices or onboard a marine drone without requiring heavy cloud computing dependencies. • Expected Solution Teams are expected to deliver a functional, modular software prototype containing the following core components: • Object Detection / Semantic Segmentation Model: An AI/ML architecture (such as YOLO,Faster R-CNN, or U-Net) trained to detect and draw bounding boxes or pixel-level masks around man-made objects (including shipwrecks, pipes, cylinders, and entangled debris nets). • Confidence Scoring & Noise Filtering Module: An algorithmic pipeline or pre-processing filter that minimizes false positives caused by natural acoustic shadows or rock clusters,outputting a clear confidence score (0% to 100%) for every detected anomaly. • Anomalous Reporting & Geotagging Engine: A data-parsing script or lightweight dashboard interface that reads sonar metadata (such as coordinate files or ping headers) to output a structured report (JSON or CSV format). This report must detail the exact location (latitude/longitude), bounding dimensions, and classification of each detected hazard. • User Interface (UI) Dashboard: A visual interface where a user can upload a raw sonar image log, view the AI models' detections overlaid on the map in real-time, and download the generated anomaly reports.

## What the brief never says

- labeled side-scan sonar training dataset
- exact sonar metadata format or coordinate file structure
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- Known obstacles in the brief: Restricted data.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from Ministry of Earth Sciences (MoES)'s published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (418 words)

• Background The accumulation of anthropogenic (man-made) debris in marine ecosystems poses a critical threat to global biodiversity. Among the most destructive types of pollution are â€˜ghost netsâ€™â€”abandoned, lost, or discarded fishing gear. These nets continuously trap and kill marine life,destroy coral reefs, and damage commercial vessel propellers. 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. • Description Participants must develop an end-to-end automated computer vision pipeline capable of ingesting side-scan sonar imagery, identifying man-made debris against a complex natural background, and generating actionable localized data.The software system must be robust enough to handle the core challenges inherent to acoustic imagery: high speckle noise, varying pixel resolutions, acoustic shadows, and data dropouts caused by underwater vehicle motion (heave, pitch, and roll). The primary objective is to build an algorithm that reliably separates natural seafloor topology from artificial anomalies. The final solution should be optimized to run efficiently, potentially allowing deployment on edge devices or onboard a marine drone without requiring heavy cloud computing dependencies. • Expected Solution Teams are expected to deliver a functional, modular software prototype containing the following core components: • Object Detection / Semantic Segmentation Model: An AI/ML architecture (such as YOLO,Faster R-CNN, or U-Net) trained to detect and draw bounding boxes or pixel-level masks around man-made objects (including shipwrecks, pipes, cylinders, and entangled debris nets). • Confidence Scoring & Noise Filtering Module: An algorithmic pipeline or pre-processing filter that minimizes false positives caused by natural acoustic shadows or rock clusters,outputting a clear confidence score (0% to 100%) for every detected anomaly. • Anomalous Reporting & Geotagging Engine: A data-parsing script or lightweight dashboard interface that reads sonar metadata (such as coordinate files or ping headers) to output a structured report (JSON or CSV format). This report must detail the exact location (latitude/longitude), bounding dimensions, and classification of each detected hazard. • User Interface (UI) Dashboard: A visual interface where a user can upload a raw sonar image log, view the AI models' detections overlaid on the map in real-time, and download the generated anomaly reports.