一个代的噪音注释校正模型,用于强大的植物疾病检测检测
Jiuqing Dong1,2, Alvaro Fuentes1,2, Sook Yoon3
1Department of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea.
Frontiers in plant science
|November 9, 2023
概括
本研究引入了一种教师-学生学习方法,通过纠正培训数据中的不准确的界限框来改善植物疾病检测. 这种方法提高了模型的稳定性,减少了对完美的注释的需求,降低了精准农业的成本.
科学领域:
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
- 机器学习 机器学习
背景情况:
- 用于检测植物疾病的物体探测器对杂的训练数据和不准确的注释敏感.
- 高质量的注释数据集是必不可少的,但创建成本高,耗时长.
- 现实世界农业数据往往包含位置噪声在边界框注释.
研究的目的:
- 开发一种方法,从带有不准确边界框的植物疾病图像中学习可靠的特征表示.
- 为了减少模型对精确注释质量的依赖.
- 为了减轻农业数据集中的噪音标签的影响.
主要方法:
- 对现实世界噪音注释分布的分析.
- 实施教师-学生学习范式,以纠正不准确的界限框.
- 教师模型纠正了杂的边界框;学生模型从纠正的数据中学习了强大的功能.
主要成果:
- 当应用到Faster-RCNN检测器时,在杂的数据集上实现了26%的性能改进.
- 达到大约75%的完全监督性能,只有1%的标签可用.
- 证明了对半监督学习和自动标签的概括性.
结论:
- 提出的教师-学生方法有效地解决了植物疾病检测的界限框注释中的真实世界位置噪声.
- 这种方法减轻了精准农业中噪音数据所带来的挑战,优化了数据标签.
- 鼓励对植物疾病检测和智能农业进行低成本的研究.
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