一个改进的YOLOv5s模型使用特征连接与注意力机制来实时检测和计数水果
Olarewaju Mubashiru Lawal1, Shengyan Zhu1, Kui Cheng1
1Sanjiang Institute of Artificial Intelligence and Robotics, Yibin University, Sichuan, China.
Frontiers in plant science
|July 12, 2023
概括
增强的YOLOv5s模型可以在复杂的环境中改善实时果实检测. 这种轻量级模型为机器人水果采摘等应用提供了更低的计算能力和更高的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 实时对象检测对于自动化系统至关重要.
- 复杂的环境对现有的检测模型构成挑战.
研究的目的:
- 开发一个改进的YOLOv5s模型,以高效准确地检测水果.
- 验证模型在复杂条件下的新水果数据集上的性能.
主要方法:
- 将特征连接和注意力机制纳入YOLOv5s架构.
- 在新的水果数据集上进行验证,评估检测准确性,速度和计算成本.
- 与其他YOLO变种进行比较,例如GhostYOLOv5s,YOLOv4-tiny和YOLOv7-tiny.
主要成果:
- 改进的YOLOv5s模型显示了更小的尺寸 (45.5%更少的层,30.2%更少的参数) 和计算负载 (14.1%更少的GFLOP,31.3%更小的重量尺寸).
- 实现了较高的平均精度 (mAP) 93.4% (有效集) 和96.0% (测试集),速度为74fps.
- 在检测准确性和效率方面表现优于其他主流YOLO变体,在水果跟踪和计数方面错过和不正确的检测较少.
结论:
- 改进的YOLOv5s是一个轻量级且计算效率高的模型,适合实时检测.
- 它在复杂的环境中很好地泛化,适用于水果采摘机器人和低功耗设备.
- 该模型在农业应用中比现有的YOLO变体提供了显著的进步.
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