基于改进的YOLOv5s的海鲜物体检测方法
Nan Zhu1,2, Zhaohua Liu1,2, Zhongxun Wang1,2
1School of Physics and Electronic, Yantai University, Yantai 264005, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种使用YOLOv5s的改进的水下物体检测方法,其中包括一种新的空间通道协同注意模块和一个双路径可变内核模块. 改进的模型实现了更高的准确性和效率,用于检测水生目标,如海黄瓜.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 海洋生物学 海洋生物学
背景情况:
- 传统的水下物体检测算法与错误阳性和错过检测作斗争.
- 准确识别水生物种对于海洋研究和资源管理至关重要.
研究的目的:
- 提高水下海鲜物体检测的准确性和效率.
- 为了减少水生目标识别中的错误阳性和错误检测.
主要方法:
- 开发了一种基于YOLOv5s.的改进检测方法.
- 引入了一个空间通道协同注意 (SCSA) 模块,以增强目标特征并抑制背景噪声.
- 集成了一个三级卷积双路径可变内核模块 (C3k2-PSConv),以改善多维特征提取,特别是对于小或封闭的目标.
主要成果:
- 增强的YOLOv5s模型在URPC数据集上实现了2.3%的平均平均精度 (mAP) 增加.
- 将模型参数数量减少了大约2.4%,保持了实时推断速度.
- 对水下物体检测的操作效率显著提高.
结论:
- 拟议的方法有效地提高了在复杂的水下环境中水生目标的检测.
- 集成SCSA和C3k2-PSConv模块为实时,准确的水下物体检测提供了一个有希望的方法.
- 改进后的模型显示了在海洋监测和渔业方面的应用潜力.
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