SIH26057SoftwareMinistry of Earth Sciences (MoES)

AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using Side-Scan Sonar Imagery

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 ‘ghost nets’-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 behi

The Ask

Participants must develop an end-to-end automated computer vision pipeline that can ingesting side-scan sonar imagery, identifying man-made debris against a complex natural

Real Struggle

Among the most destructive types of pollution are ‘ghost nets’-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

Expected Solution

Participants must develop an end-to-end automated computer vision pipeline that can ingesting side-scan sonar imagery, identifying man-made debris against a complex natural

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...
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...
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...
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...

AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using Side-Scan Sonar Imagery

Background

The Ask

Participants must develop an end-to-end automated computer vision pipeline that can ingesting side-scan sonar imagery, identifying man-made debris against a complex natural

Real Struggle

Expected Solution

Participants must develop an end-to-end automated computer vision pipeline that can ingesting side-scan sonar imagery, identifying man-made debris against a complex natural

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...
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...
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...
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...