一个基于改进的YOLOv7的封闭桃番茄识别模型
Guangyu Hou1,2, Haihua Chen3, Yike Ma3
1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
Frontiers in plant science
|November 6, 2023
概括
一个新的深度学习模型,DSP-YOLOv7-CA,准确地识别了机器人采摘所隐藏的桃番茄. 这种模型提高了检测准确度,减少了参数,增强了农业自动化.
科学领域:
- 农业机器人农业机器人
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 堵塞是自动化桃番茄收获机器人的一个主要挑战.
- 准确地识别塞住的西红对于高效的机器人采摘至关重要.
研究的目的:
- 开发一种高效准确的深度卷积神经网络模型,用于识别封闭的桃番茄.
- 为了提高桃番茄采摘机器人在自然环境中的性能.
主要方法:
- 构建了一个桃番茄数据集 (TOSL),具有不同的闭塞水平.
- 修改了YOLOv7架构,结合了深度可分离的卷积和协调注意力 (CA).
- 用深度可分离的卷积式SPPF模块取代了SPPCSPC,以保存小目标信息.
主要成果:
- 拟议的DSP-YOLOv7-CA模型实现了98.86%的平均检测精度 (mAP).
- 模型参数从37.62MB减少到33.71MB.
- 在检测低于95%封闭度的桃番茄方面表现出卓越的性能.
结论:
- 在自然环境中,DSP-YOLOv7-CA有效地识别了封闭的桃番茄.
- 该模型为提高桃番茄采摘机器人的精度提供了可行的解决方案.
- 对于极高的遮水平 (>95%) 可能需要进一步改进.
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