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 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.