内向外:转换实验室种植植物的图像,用于农业中的机器学习应用
Alexander E Krosney1,2, Parsa Sotoodeh3, Christopher J Henry3
1Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.
Frontiers in artificial intelligence
|July 24, 2023
概括
合成生成的野外图像使用对比的未配对翻译 (CUT) 生成对抗网络 (GAN) 提高了植物检测的准确性. 使用这些合成数据集训练对象检测模型可以提高性能,而不是仅使用真实数据.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习在农业中的应用
背景情况:
- 机器学习模型需要大量的训练数据,在农业中由于植物表型的自然变化,这种数据往往很少.
- 合成数据增强可以解决当现实世界数据集不足时的数据限制.
研究的目的:
- 开发一种方法,从室内图像中生成植物的现实现场图像.
- 评估合成生成数据的有效性,用于培训植物检测模型.
主要方法:
- 利用一个对比的未配对翻译 (CUT) 生成对抗网络 (GAN) 来将室内植物图像翻译成现场图像.
- 扩展了该方法以生成多种植物现场图像.
- 使用真实和合成数据集训练了YoloV5纳米物体检测模型.
主要成果:
- CUT-GAN成功地将单植物室内图像翻译成现场图像,并扩展到多植物场景.
- 使用合成增强数据训练的物体检测模型在真实现场图像上表现出更好的准确性,与仅使用真实数据训练的模型相比.
结论:
- 使用CUT-GAN生成合成数据是增强农业数据集的可行策略.
- 拟议的方法有效地提高了物体检测模型在现场条件下用于植物识别的性能.
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