Automatic Asbestos Control Using Deep Learning Based Computer Vision System
نویسندگان
چکیده
The paper discusses the results of research and development an innovative deep learning-based computer vision system for fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins open pit within time comparable with work specialists (about 10 min per one processing place). discussed is based on applying instance semantic segmentation artificial neural networks. Mask R-CNN-based network architecture applied to asbestos-containing chunks searching images pit. U-Net-based selected chunks. designed allows search takes rocks near-infrared range (NIR) processes obtained images. result average each controlled It validated estimate as graduated ratio vein area value value, both determined by trained network. For training tasks training, validation, test datasets are collected. demonstrates error about 0.4% under different weather conditions when 1.5–4%. accuracy sufficient use a geological service tool instead currently visual-based estimations.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app112210532