# Lightweight robust detection of anthropogenic floating debris in turbid and dynamic aquatic environments via enhanced feature fusion

## TL;DR

This paper introduces a new lightweight model for detecting man-made floating debris in polluted water, which is more accurate and efficient than existing methods.

## Contribution

The novel BiDB-YOLOv8 model and the newly created Turbid-floater dataset for AFD detection in complex aquatic environments.

## Key findings

- BiDB-YOLOv8 improves detection accuracy with minimal parameter increase compared to YOLOv8n.
- It matches YOLOv8s accuracy but with significantly lower computational cost.
- The model is robust in turbid and dynamic aquatic environments.

## Abstract

Accurate detection of Anthropogenic Floating Debris (AFD) is crucial for water pollution management. However, existing detection algorithms often lack environmental robustness in complex aquatic environments, and their high computational costs also impede deployment on lightweight platforms. To address these issues, this study proposes BiDB-YOLOv8, with an enhanced feature processing architecture. The model first employs a multi-branch convolutional block to improve the quality of extracted features, enhancing its ability to distinguish small targets from noisy backgrounds. It then uses an efficient bidirectional fusion network to ensure these high-quality features are effectively integrated across different scales. Additionally, this paper constructs and releases an original dataset, Turbid-floater, which includes diverse interference scenarios. Experimental results show that BiDB-YOLOv8 improves detection accuracy over the baseline YOLOv8n with a negligible increase in parameters. Compared to the larger YOLOv8s, it achieves nearly identical accuracy with only a fraction of the computational cost, highlighting a favorable trade-off between precision, robustness, and efficiency for real-world environmental monitoring.

The online version contains supplementary material available at 10.1038/s41598-025-31043-9.