基于改进的YOLOX的基础上,西红的成熟度和茎的识别
Yanwen Li1, Juxia Li2, Lei Luo1
1College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, 030800, China.
Scientific reports
|January 14, 2025
概括
本研究介绍了YOLOX-SE-GIoU模型,通过提高果实成熟度和茎的识别来增强番茄收获. 该模型实现了高精度,大大减少了智能收获系统中的错误.
科学领域:
- 计算机视觉 计算机视觉
- 农业机器人农业机器人
- 机器学习 机器学习
背景情况:
- 智能收获面临着不平衡的番茄果实成熟度水平和果实和茎的识别精度低的挑战.
- 现有的模型在农业应用中与规模变化和阶级不平衡作斗争.
研究的目的:
- 开发一个改进的物体检测模型,用于精确识别番茄果实成熟度和茎的智能收获.
- 在番茄检测数据集中解决阶级不平衡和规模变化.
主要方法:
- 提出了YOLOX-SE-GIoU模型,将SE焦点模块集成到YOLOX中,以提高识别精度.
- 将损失函数优化为GIoU损失,以处理水果和茎的尺度差异.
- 对YOLOv4,YOLOv5,YOLOv7和原来的YOLOX进行了评估.
主要成果:
- YOLOX-SE-GIoU模型实现了92.17%的平均平均精度 (mAP).
- 在半熟番茄 (1.6826.66%的增加) 和茎 (3.7845.03%的增加) 的平均精度 (AP) 显著改善.
- 它的性能优于现有的模型,与YOLOv4,YOLOv5,YOLOv7和YOLOX相比显示1.1722.21%更高的mAP.
结论:
- YOLOX-SE-GIoU模型为不平衡和尺度变化的番茄样品提供了卓越的识别性能.
- 有效地减少错误和错过的检测,提高自动化番茄收获的准确性.
- 为推进水果收获自动化提供了基础技术.
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