用注意力机制提升YOLOv8水下目标检测精度
EPBC-YOLOv8: An efficient and accurate improved YOLOv8 underwater detector based on an attention mechanism
- 在骨干网络中加入通道与空间注意力，增强特征提取能力
- 在URPC2019和2020数据集上分别达到76.7%和79.0%的[email protected]
- 适合海洋生物检测、水下视觉系统等实际应用
本研究通过在YOLOv8的骨干网络中引入通道与空间注意力机制，采用Pointwise Convolution构建FasterPW模型，并在受BiFPN启发的WFPN结构中使用加权拼接以增强跨尺度连接与鲁棒性。结合CARAFE进行精细化特征重组装，有效缓解水下图像退化问题。在URPC2019和URPC2020数据集上，[email protected]分别达到76.7%和79.0%，较原始YOLOv8提升2.3%和0.7%，显著提升了对海洋生物的检测精度。
原文摘要 · Abstract (English)
In this study, we enhance underwater target detection by integrating channel and spatial attention into YOLOv8's backbone, applying Pointwise Convolution in FasterNeXt for the FasterPW model, and leveraging Weighted Concat in a BiFPN-inspired WFPN structure for improved cross-scale connections and robustness. Utilizing CARAFE for refined feature reassembly, our framework addresses underwater image degradation, achieving mAP at 0.5 scores of 76.7 percent and 79.0 percent on URPC2019 and URPC2020 datasets, respectively. These scores are 2.3 percent and 0.7 percent higher than the original YOLOv8, showcasing enhanced precision in detecting marine organisms.
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