以数据为中心的方法,例如光学废物分类中的细分
Anna Iliushina1, Gleb Mazanov1, Sergey Nesteruk1
1Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
Waste management (New York, N.Y.)
|November 8, 2024
概括
这项研究引入了一个以数据为中心的管道,以改进使用计算机视觉对家庭废物进行分类. 通过将伪注释和数据增强相结合,该系统显著提高了废物细分的准确性,使分类更有效和更具成本效益.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 环境技术 环境技术
背景情况:
- 计算机视觉系统为家庭垃圾分类提供了更高的效率和成本降低.
- 目前的系统面临的局限性是由于数据注释差,具有挑战性的识别环境,以及可见距离摄像头的数据不足.
研究的目的:
- 开发一个以数据为中心的管道,以提高输送带上的多类家庭废物细分的精度.
- 解决现有计算机视觉系统在废物分类应用中的局限性.
主要方法:
- 开发了一个以数据为中心的管道,包括数据平衡,转移学习和伪标签.
- 使用伪注释方法与基于对象的数据增强算法相结合.
- 准备了5000个手动标记的数据集和10,000个伪标记的数据点.
主要成果:
- 该管道将YOLOV8细分模型的平均平均精度 (mAP) 从简单场景的67%提高到83%.
- 对于复杂的工业解决方案,mAP从42%增加到59%.
- 在"简单"图像上成功训练模型,在"复杂"图像上获得满意的结果.
结论:
- 拟议的以数据为中心的管道有效地提高了基于计算机视觉的废物细分的精度.
- 伪注释和基于对象的增强是克服数据质量和数量限制的关键.
- 该方法提供了一种具有成本效益的解决方案,用于提高自动化废物分类系统的效率.
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