YOLOv8-C2f-Faster-EMA:基于YOLOv8的改进的水下垃圾检测模型
Jin Zhu1, Tao Hu1, Linhan Zheng1
1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
这项研究引入了改进的YOLOv8算法,用于检测水下小碎片,提高水污染监测的准确性和减少错误. 这一进步有助于海洋保护和遥感应用.
科学领域:
- 环境科学 环境科学
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 水生环境中的人为废物会降低水质,影响生态系统和人类健康.
- 水下机器人技术为识别和清除水下垃圾提供了解决方案.
- 现有的检测算法面临的挑战是,水生环境中小碎片的错误检测率和错误检测率很高.
研究的目的:
- 开发一种精细的算法,以更好地检测小规模的水下碎片.
- 为了减轻水生垃圾检测中的高错误和错误检测率.
- 提高水下碎片识别的准确性和计算效率.
主要方法:
- 介绍YOLOv8-C2f-Faster-EMA算法,优化骨干,部层和C2f模块用于水下条件.
- 整合一个有效的注意力机制,以提高检测能力.
- 与传统的YOLOv8n框架进行实证比较.
主要成果:
- 拟议的YOLOv8-C2f-Faster-EMA算法与YOLOv8n.相比显示出更高的性能.
- 实现了精度 (P) 的6.7%,回忆 (R) 的4.1%,平均平均精度 (mAP) 的5%提升.
- 该算法提高了水下垃圾检测准确度,同时简化了计算模型.
结论:
- 精致的YOLOv8-C2f-Faster-EMA算法显著提高了水下碎片的检测.
- 这种方法为海洋保护和污染监测提供了一个有前途的工具.
- 潜在的应用范围包括遥感,提高局部监控精度.
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