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人工智能的快速纤维检测技术用于模拟大气样本的相对比显微镜图像中

Takashi Yamamoto¹, Kazuharu Iwasaki², Yukiko Iida³

¹National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.

Annals of work exposures and health

|March 4, 2024

概括

人工智能 (AI) 显著加快了空气样本中石棉检测的速度. 一个多层聚合网络 (MA-Net) 模型在从显微镜图像中识别石棉纤维方面取得了高准确性,改进了传统方法.

科学领域:

环境科学环境科学
职业健康职业健康职业健康
分析化学分析化学

背景情况:

日本禁止使用石棉,但旧建筑的拆除仍然是大气中石棉的主要来源.
目前用于检测空气中的石棉纤维的方法耗时,阻碍了早期干预.
快速检测对于监测和控制建筑拆除期间的石棉排放至关重要.

研究的目的:

研究人工智能 (AI) 在空气样本中快速检测石棉纤维的有效性.
为了比较不同的人工智能模型在分析石棉相对比显微镜图像中的性能.
减少环境监测中石棉纤维计数所需的时间.

主要方法:

准备了含有阿莫司特和里索的模拟大气样本.
图像是使用相对比显微镜捕获的.
两个人工智能模型,Mask R-CNN和MA-Net,在专家注释的数据集上进行了训练,用于光纤检测.

主要成果:

该MA-Net模型显示了高准确性,在石棉纤维检测中达到95%的回忆率和91%的精度.
面具R-CNN模型的性能较低,回忆率为57%,精度为46%.
两种AI模型都在每张图像下1秒处理图像,比手动计数快得多.

结论:

人工智能驱动的图像分析,特别是使用MA-Net模型,提供了一种快速而准确的方法来检测大气中的石棉纤维.
这项技术可以显著提高监测建筑拆除和拆除期间石棉排放的效率.
开发的AI方法有望提高与石棉有关的职业安全和环境保护.

关键词:

人工智能的人工智能是人工智能.石棉是一种石棉.相对比显微镜的相对比显微镜.快速检测快速检测迅速检测

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