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Abstract：
Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models-an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)-that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst.
摘要：
自制造以来，进口,日本已经完全废除了石棉产品的使用，石棉排放到大气中的主要原因是拆除和拆除用含石棉材料建造的建筑物。及早发现和纠正不适当的拆除和清除作业所产生的石棉排放，需要一种快速的方法来测量大气中的石棉纤维。当前的快速测量方法是短期大气采样和相差显微镜计数的组合。然而,视觉计数需要相当长的时间并且不够快。使用人工智能（AI）分析显微镜图像以检测纤维可能会大大减少计数所需的时间。因此,在这项研究中，我们研究了使用AI图像分析来检测相差显微镜图像中的纤维。使用相差显微镜观察了由铁石棉和温石棉的标准样品制备的一系列模拟大气样品。图像被捕获，和培训数据集是根据专家分析师的计数结果创建的。我们采用了两种类型的人工智能模型——实例分割模型，即基于掩模区域的卷积神经网络(MaskR-CNN)，和语义分割模型，即多级聚合网络（MA-Net）-经过训练可以检测石棉纤维。使用MaskR-CNN模型实现的光纤检测准确率为57%的召回率和46%的准确率，而MA-Net模型的召回准确率为95%，准确率为91%。因此,MA-Net模型得到了满意的结果。在两种AI模型中，光纤检测所需的时间均小于1s/图像，这比专家分析师计数所需的时间要快。
