FEB-YOLOv8:用于水下物体检测的多尺度轻量级检测模型
Yuyin Zhao1, Fengjie Sun1, Xuewen Wu1
1Department of Cyberspace Security, Hainan University, Haikou, Hainan Province, China.
PloS one
|September 27, 2024
概括
这项研究介绍了FEB-YOLOv8,一个轻量级的水下物体检测模型,解决了机器人的局限性. 它实现了更高的准确性和更低的计算负载,为海洋资源管理提供了有效的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 海洋技术 海洋技术
背景情况:
- 水下物体检测对于海洋资源管理至关重要.
- 水下机器人面临的挑战是有限的存储和计算能力.
- 现有的检测模型通常在海洋环境中难以提高效率和准确性.
研究的目的:
- 为水下机器人开发一种新的轻量级物体检测模型.
- 提高水下物体检测系统的效率和准确性.
- 解决水下机器人应用中有限的存储和计算能力的限制.
主要方法:
- 建议FEB-YOLOv8,这是一个基于YOLOv8框架的轻量级模型.
- 通过精制的C2f和新的P-C2f模块来增强骨干网络.
- 包含EMA模块以改进多尺度特征提取和以Bi-FPN为灵感的特征金字塔网络以获得平衡的性能.
主要成果:
- FEB-YOLOv8在DUO和URPC2020数据集上分别实现了1.2%和1.3%的mAP增长.
- 与基线相比,减少了6.2G GFLOPs (24.39%的减少) 和1.64M参数 (45.51%的减少) 的计算负载.
- 在模型轻度和检测精度之间的平衡中显著改善.
结论:
- FEB-YOLOv8为水下物体检测提供了一个有利的解决方案.
- 该模型有效地将轻度与精度相协调,适合资源有限的水下机器人.
- 拟议的修改提高了在海洋环境中的特征提取和检测性能.
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