The purpose of this work is to study the applicability of the U-Net architecture for automatically determining the contours of natural skins using the TensorFlow and Keras libraries in Python. A software application has been developed based on methods including OpenCV libraries, as well as a model for implementing a deep convolutional neural network. The dataset for training and testing the network was created using augmentation. Training was carried out using the stochastic gradient descent method after splitting the data sample into training and test images. In the future, the results obtained will be used to create an automated system that will make it possible to determine the contours of the skin and its defects, which in turn will open up the possibility of calculating the useful area of ​​the skin and creating an automated layout of patterns taking into account the identified defects.

Keywords: computer vision, edge detection, natural skin, machine learning, convolutional neural networks, U-Net architecture, deep learning

The problem of developing an intelligent automated system for detecting defects in textile materials is considered. An analysis of machine learning and deep learning algorithms was carried out in relation to solving the problem of product quality control. The implementation of an artificial neural network implemented in a Raspberry Pi microcomputer and receiving a set of input data in the form of a large stream of images from a high-speed digital camera is considered. The stages of creating a model in Python using the TensorFlow and Keras libraries are described. The development process includes the preparation of initial data intended for training and testing the system, as well as testing the operation of the resulting neural network, which consists in recognizing images of defects on fabric according to classification criteria.

Keywords: machine learning, neural network, defect images, textile material, training, testing, accuracy

The control of the fabric in the technological chain determines the structure of this very chain. The article uses a probabilistic model to assess the feasibility of re-processing fabric in finishing production.

Keywords: fabric, marriage, quality, batch, product, technological defect, reprocessing, profit, loss

The article discusses the issue of creating an automated system for detecting defects on tissue based on the use of computer vision. The resulting system makes it possible to control, register and calculate defects in textile materials without human participation in the technological process, which improves the quality of analysis, eliminates the number of errors in fabric sorting and reduces the cost of the technological operation.

Keywords: automated system, defect detection, textile material, computer vision, microcomputer, image processing library