An enhanced YOLOv8 model for accurate detection of solid floating waste.

To address the challenges in floating waste detection on water surfaces, such as small object scale, irregular shapes, and strong background interference, this study proposes an enhanced detection model based on the YOLOv8s frame work, named ES-YOLOv8. The new model optimizes the feature fusion strategy in the neck, constructing a refined "160-80-40-20" multiscale detection frame work. Integrated with the Efficient Multiscale Attention (EMA) module, it significantly improves the model's ability to extract features of small float ing objects. Additionally, an innovative Shape-IoU loss function is employed to optimize the bounding box regression accuracy of irregular targets through shape-sensitive constraints. This results in the development of an enhanced model that integrates feature enhancement, interference suppression, and localization optimization. Experimental results in a self-constructed floating waste dataset demonstrate that, compared to baseline YOLOv8s, the ES-YOLOv8 algorithm improves mAP@0.5 and mAP@0.5:0.95 by 5.4% and 6.1%, respectively. Compar ative experiments with state-of-the-art models further validate its superiority and effectiveness. Furthermore, experiments conducted on public datasets confirm the robustness and generalizability of ES-YOLOv8. This study aims to provide a high-precision, low-power-consumption technological solution for intelligent water governance, offering potential ecological and engineering applications.