[2Win5-71] The method of detecting asbestos in electron microscopy images by focusing background.
Keywords:AI, segmentation
Asbestos monitoring is mandatory at demolition sites to prevent atmospheric leakage. One procedure involves using electron microscopy images to observe air-sampled filters, counting fibrous substances, and then identifying asbestos through elemental analysis. Although several hundred images are taken per filter, we need reducing false positives to minimize time-consuming elemental analyses.
Images from electron microscopy are monochrome, making asbestos detection difficult due to its thin, variable shape and the similarity to filter material. Manual processing is time-consuming, and existing automatic detection methods often make many false positives. Higher magnification images improve asbestos shape clarity but reduce detection speed.
Therefore, we have considered a method for detecting asbestos using an AI-based segmentation technology without fine-tuning nor retraining. Our method aims to reduce false positives and total processing time so that we report on the method with the segmentation technology.
Images from electron microscopy are monochrome, making asbestos detection difficult due to its thin, variable shape and the similarity to filter material. Manual processing is time-consuming, and existing automatic detection methods often make many false positives. Higher magnification images improve asbestos shape clarity but reduce detection speed.
Therefore, we have considered a method for detecting asbestos using an AI-based segmentation technology without fine-tuning nor retraining. Our method aims to reduce false positives and total processing time so that we report on the method with the segmentation technology.
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