title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of crystal quality recognition algorithm based on machine learning
Creators: name: ÐÐ¸ ÐÑÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐºÐ°ÑÐµÑÑÐ²Ð¾ ÐºÑÐ¸ÑÑÐ°Ð»Ð»Ð¾Ð²
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
crystal quality
image recognition

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description: Robotic automation of the crystal sorting process in the mining industry is an imminent challenge, for which it is necessary to develop new methods and algorithms. The most effective method is to use deep learning and image processing techniques to automatically sort crystals. Conventional crystal sorting relies on manual inspection of crystals for defects, mainly by visual inspection. This method relies on the human eye to inspect and determine the quality of the crystals directly by marking them with the aid of auxiliary equipment such as flashlights and magnifying glasses. The manual sorting method mainly relies on the experience of the sorter to classify the crystals into high quality, medium quality and low quality according to the differences in color, shape, size and transparency of the crystals. Traditional methods for sorting crystals are costly and inefficient. In this paper, we propose a method that relies on deep learning and image processing techniques to automatically sort crystals. Seven non-orthogonal images of the crystals are acquired by the camera, and then the images are processed to enhance the binning features, and then a special neural network model is constructed to recognize the crystal images. In the process of image processing, operations such as edge detection are mainly used to identify the contour boundary of the crystal in the image, and the contour boundary reflects the shape and size of the crystal, which is an important basis for crystal sorting. In building the neural network model, since a crystal corresponds to 7 non-orthogonal images, 7 small neural networks are constructed first, and then the output results of the 7 small neural networks are logically combined to build a special large neural network, and finally this special neural network outputs the crystal recognition results.In this paper, the neural network model is trained by using the crystal image after image enhancement, and the recognition results are obtained with high accuracy and displayed visually through the user interface. The software environment used in this paper is Matlab.