RTDETR-MARD: A Multi-Scale Adaptive Real-Time Framework for Floating Waste Detection in Aquatic Environments

Abstract

Accurate and efficient detection of floating waste is crucial for environmental protection and aquatic ecosystem preservation, yet remains challenging due to environmental interference and the prevalence of small targets. To address these limitations, we propose a Multi-scale Adaptive Real-time Detector (RTDETR-MARD) based on RT-DETR that introduces three key innovations for improved floating waste detection using unmanned surface vessels (USVs). First, our hierarchical multi-scale feature integration leverages the gather-and-distribute mechanism to enhance feature aggregation and cross-layer interaction. Second, we develop an advanced feature fusion module incorporating feature alignment, Information Fusion, information injection, and Scale Sequence Feature Fusion components to ensure precise spatial alignment and semantic consistency. Third, we implement the Wise-IoU loss function to optimize localization accuracy through high-quality anchor supervision. Extensive experiments demonstrate the framework’s effectiveness, achieving state-of-the-art performance of 86.6% mAP50 at 96.8 FPS on the FloW dataset and 49.2% mAP50 at 107.5 FPS on our custom water surface waste dataset. These results confirm RTDETR-MARD’s superior accuracy, real-time capability, and robustness across diverse environmental conditions, making it particularly suitable for practical deployment in ecological monitoring systems where both speed and precision are critical requirements.

Keywords

• real-time object detection
• floating waste monitoring
• adaptive feature aggregation
• multi-scale feature fusion