River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index

TL;DR

This paper introduces a patch-wise classification method with mixture augmentation to detect river surface scum, enabling improved online monitoring and decision-making for river cleaning based on a novel scum-index.

Contribution

It presents a new patch-wise detection pipeline with mixture augmentation and a scum-index for real-time river surface monitoring, addressing challenges of sparse and unstable scum patterns.

Findings

Effective detection of river scum in diverse conditions

Enhanced monitoring accuracy with mixture augmentation

Potential for automated river cleaning decisions

Abstract

Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and contributes to the river's odor and has external economic effects on the landscape. Because of its feature of a sparsely distributed and unstable pattern of organic shape, automating the monitoring process has proved difficult. We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers. Furthermore, we propose a scum-index cover on rivers to help monitor worse grade online, collect floating scum, and decide on chemical treatment policies. Finally, we demonstrate the application of our method on a time series dataset with frames every ten minutes recording river scum events over several days. We discuss the significance of our pipeline and its experimental findings.