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<p>Detecting floating objects on water surfaces is critical for environmental monitoring and pollution control. We present SEDS-YOLOv8, an enhanced YOLOv8n variant that integrates Squeeze-and-Excitation (SE) attention, Distribution Shift Convolution (DSConv), and Enhanced Intersection over Union (EIoU) loss to address challenges in complex aquatic environments. These environments are characterized by surface reflections, ripple-induced noise, and dense small debris that complicate accurate detection. We trained and evaluated our model on a hybrid dataset of 28,000 images with data augmentation for robustness. The SEDSConv module replaces selected convolutional layers with DSConv for efficient multi-scale feature extraction, while SE attention suppresses reflection-induced channel noise by recalibrating feature responses. The EIoU loss accelerates convergence and improves localization accuracy through decoupled width-height regression. SEDS-YOLOv8 achieves 86.02% precision, 85.01% recall, and 88.82% mAP@0.5 with 2.90M parameters and 7.60 GFLOPs (7.3% fewer than the baseline YOLOv8n at 8.20 GFLOPs), while maintaining real-time inference at 103.7 FPS on NVIDIA RTX 4090 hardware. Our contribution is the systematic integration and adaptation of existing techniques to water-surface detection, demonstrating that task-specific architectural choices can substantially improve accuracy without sacrificing computational efficiency. Code and dataset are publicly available.</p>

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Enhanced YOLOv8 for accurate and efficient floating object detection on water surfaces

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • YanPeng Cao,
  • HaoWen Luo,
  • MengDi Wang,
  • Yue Wang,
  • Hao Yan

摘要

Detecting floating objects on water surfaces is critical for environmental monitoring and pollution control. We present SEDS-YOLOv8, an enhanced YOLOv8n variant that integrates Squeeze-and-Excitation (SE) attention, Distribution Shift Convolution (DSConv), and Enhanced Intersection over Union (EIoU) loss to address challenges in complex aquatic environments. These environments are characterized by surface reflections, ripple-induced noise, and dense small debris that complicate accurate detection. We trained and evaluated our model on a hybrid dataset of 28,000 images with data augmentation for robustness. The SEDSConv module replaces selected convolutional layers with DSConv for efficient multi-scale feature extraction, while SE attention suppresses reflection-induced channel noise by recalibrating feature responses. The EIoU loss accelerates convergence and improves localization accuracy through decoupled width-height regression. SEDS-YOLOv8 achieves 86.02% precision, 85.01% recall, and 88.82% mAP@0.5 with 2.90M parameters and 7.60 GFLOPs (7.3% fewer than the baseline YOLOv8n at 8.20 GFLOPs), while maintaining real-time inference at 103.7 FPS on NVIDIA RTX 4090 hardware. Our contribution is the systematic integration and adaptation of existing techniques to water-surface detection, demonstrating that task-specific architectural choices can substantially improve accuracy without sacrificing computational efficiency. Code and dataset are publicly available.