FCOS-LSC:在复杂的果园环境中检测绿色水果的新型模型
Ruina Zhao1, Yujie Guan1, Yuqi Lu1
1School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
Plant phenomics (Washington, D.C.)
|July 21, 2023
概括
一个新的FCOS-LSC模型通过使用注意力机制来提高机器视觉准确度来增强绿色水果的检测. 这种方法有效地识别果实,尽管封闭和照明变化,超越现有的模型.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 农业技术 农业技术
背景情况:
- 由于遮蔽,照明和视角,准确识别绿色水果是具有挑战性的.
- 现有的机器视觉模型在自然农业环境中难以识别绿色水果的复杂性.
研究的目的:
- 开发一个优化的水果检测模型,以更好地识别绿色水果.
- 提高水果检测系统在智能农业中的稳定性和通用性.
主要方法:
- 开发了一种经过优化的FCOS (全卷积一阶段物体检测) 模型,命名为FCOS-LSC.
- 集成的ResNet50具有可变形卷积用于特征提取和特征金字塔网络 (FPN) 用于信息融合.
- 集成的LSC (水平尺度,空间,通道) 注意障碍,以增强跨多维的特征感知.
主要成果:
- 在FCOS-LSC模型中,绿果的平均精度为63.0%,绿鱼的平均精度为75.2%.
- 在精度和计算复杂性方面,在最先进的模型上表现出卓越的性能.
- 成功地解决了诸如重叠的遮蔽,多样化的照明和不同的捕捉角度等挑战.
结论:
- FCOS-LSC模型为智能农业设备提供了准确和高效的绿色水果识别.
- 建议的注意力机制和样本选择策略提高了检测准确性和模型稳定性.
- 对于需要可靠的果实检测的现实世界农业应用,FCOS-LSC提供了一个有前途的解决方案.
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