基于AWGAM-YOLOv8nn的水冷墙灰积累的识别
Yongxing Hao1,2, Bin Wang2, Yilong Hao3
1School of Mechanical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China.
Scientific reports
|October 13, 2024
概括
这项研究引入了改进的YOLOv8n算法,用于精确检测废物焚烧炉壁上的灰积累. 改进后的模型可以显著降低参数,并提高机器人清洁应用的准确性.
科学领域:
- 机器人和自动化机器人与自动化
- 废物管理技术 废物管理技术
- 计算机视觉 计算机视觉
背景情况:
- 准确识别废物焚烧炉水冷墙上的灰积累对于高效的机器人手臂清洁至关重要.
- 现有的检测方法可能缺乏实时工业应用所需的速度和精度.
研究的目的:
- 开发一种新,轻量级,高精度的算法,用于检测水冷墙壁上的灰积累.
- 为了提高YOLOv8n对象检测模型的性能,用于此特定的工业任务.
主要方法:
- 实施了多级融合图像增强算法,以提高图像质量.
- 用Mobilenetv3网络替换了YOLOv8n骨干,以减少模型参数.
- 引入了一个Add Weight 全球注意力机制 (AWGAM),以增强功能集成和学习能力.
主要成果:
- 与原始模型相比,改进的YOLOv8n模型显示参数减少了59.9%.
- 在精度上提高了4.4%,在回忆中提高了8.8%,在mAP50中提高了3.2%,在mAP50-95.5中提高了8.8%.
- 该模型显示了与原始YOLOv8n相比的显著改进,以及与其他先进模型的竞争力.
结论:
- 拟议的轻量级和精确的灰积累检测模型为废物焚烧炉墙壁监测提供了有前途的应用.
- 这些改进显著提高了机器人清洁操作的模型效率和检测性能.
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