A Comparative Analysis of AI-Driven Marine Debris Detection Methods for Indian Ocean Plastic Monitoring

Nimisha Nair, Darshan Gadekar, Nalaksh Randhawa, Roshan Kotkondawar, Krushna Taiwade

Plastic pollution continues to accumulate in marine environments at an alarming rate, with millions of tonnes entering the oceans annually. Given the length of India's coastline, early detection of floating debris is essential, as undetected objects may disperse, fragment, or sink before any cleanup effort can be initiated. Satellite remote sensing paired with artificial intelligence has become the go-to tool for this problem, yet a close reading of the published work reveals something surprising: researchers rarely explain why they chose one detection method over another. Most studies simply use whatever technique fits the sensor already in hand, without weighing it against alternatives suited to the deployment at hand — a gap even more pronounced for Indian coastal waters, which remain strikingly under-studied relative to the wider global literature. Motivated by this, the present paper reviews twenty-five published studies and compares seven AI-driven detection-method families — spectral-index screening, classical machine learning, CNN and U-Net segmentation, YOLO-based detection, hyperspectral classification, SAR-based screening, and drift-forecasting models — not merely on accuracy, but on sensor cost, robustness to confounders such as algae and biofouling, and real-world deployment readiness. The review finds that spectral-index and classical machine-learning approaches remain the most field-tested and affordable, reporting 86–98% accuracy on Sentinel-2 imagery, while drift forecasting is still reported largely qualitatively, with little validation against real drift tracks, and Indian coastal research remains scattered, single-site, and hard to compare across studies. These findings point to a pressing need for low-cost, transparently reported detection frameworks built for Indian shores, alongside a coordinated regional benchmark, drift-forecasting pipelines validated against in-situ tracks, and multi-sensor fusion combining optical, hyperspectral, and SAR data.