SIH26057: AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using — SIH 2026 | Wynit

SIH26057: AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using — SIH 2026 | Wynit

What they'll ask you

11

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 morethe 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.
The brief asks for noise filtering module. How would you build that?
Decoded from SIH26057 itself: A pre-processing filter that removes false positives from natural rock clusters and acoustic shadows. The brief asks for it by name.
The brief asks for reporting and geotagging engine. How would you build that?
Decoded from SIH26057 itself: A script that reads sonar metadata and outputs structured reports in JSON or CSV format with location details. The brief asks for it by name.
The brief asks for ui dashboard. How would you build that?
Decoded from SIH26057 itself: A visual interface where users can upload sonar logs, view detections overlaid on a map, and download reports. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: 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.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

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.