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Convolutional neural networks application for dust particles detection and recognition on microscopy images

Keywords:

A.N. Kokoulin - Ph.D. (Eng.), Associate Professor, Perm National Research Polytechnic University. E-mail:liga_asu@mail.ru


This article define the design of the automated image analysis system of industrial dust particles for dust shape and component research using microscopic images of different scales. Industrial dusts are the air emulsions with hard particles of any sizes. The dust influence on population’s health is described as the superposition of particle sizes, their shape and chemical composition. The visual dust research is usually not automated. Due to this the probability of mistakes and misrecognitions is too high. And the automation of recognition process based on knowledge base of particle samples and wavelet analysis algorithms is expected to enhance the identification quality of large amounts of microscopic images. In this article we consider a novel approach based on wavelet transform for edge detection, simplified variant of active contour method for objects enumeration on image and convolutional neural networks for objects classification.
References:

 

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May 29, 2020

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