基于复杂的自然环境中改进的YOLOv8n的实时精确果检测
Mingjie Wang1,2, Fuzhong Li3
1College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Plants (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种增强的YOLOv8n模型,用于机器人系统中高效地检测果. 轻量级模型实现了更高的准确性和速度,优于现有实时应用的现有方法.
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
- 机器学习 机器学习
背景情况:
- 有效和准确的果检测对于自动果收获至关重要.
- 现有的物体检测模型往往面临着速度和准确性的挑战,特别是对于封闭的物体.
研究的目的:
- 为机器人应用开发一种轻量级和高效的果检测模型.
- 为了提高检测准确度,速度和稳定性,特别是对于部分封闭的果.
主要方法:
- 提出了一个基于YOLOv8n.的新型轻量级果检测模型.
- 引入了一种自我校准坐标 (SCC) 注意模块,用于增强特征提取.
- 集成了一种部分卷积模块,该模块通过修复参数化 (PCMR) 和多项式损失 (PolyLoss) 得到了改进.
- 来自第二和第三层金字塔的融合多尺度特征用于优化.
主要成果:
- 改进的模型实现了88.90%的mAP,比YOLOv8n.增加了2.90%.
- 检测速度提高了30.55%,达到220 FPS,参数和FLOP减少.
- 在mAP和速度上都超过了主流的对象检测算法.
- 在Android应用程序上实现了40 FPS,比YOLOv8n.n.提高了26.93%.
结论:
- 拟议的轻量级模型显著提高了机器人系统的果检测效率和准确性.
- 该模型的性能和实时功能使其适用于资源有限的环境.
- 这项研究为开发农业机器人有效检测模型提供了宝贵的参考.
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