Relevance of the topic. Detecting defects has always been and will always be an urgent task. Untimely and insufficiently reliable defect detection can lead to user dissatisfaction, loss of revenue, increased costs, safety hazards, and impact on the organization's competitiveness. Surface defects in metal products, such as cracks, stains, dents, corrosion, etc., can negatively affect the quality and durability of products. The development and application of advanced segmentation algorithms will allow you to detect potentially dangerous defects in time and take appropriate measures to eliminate them, ensuring the safety and reliability of products. Segmentation of images of surface defects in metal products can be used to automate the production quality control process. An automated defect detection system will reduce dependence on human work and increase the speed and reliability of defect detection, which will positively affect production efficiency and reduce costs. For this purpose, it is promising to use machine learning methods.
Purpose: to automate the process of segmentation of images of surface defects of metal products obtained during visual and optical flaw detection using neural network technologies.

To achieve this goal, the following tasks are necessary:
1. Analyze the current state of development of methods for detecting surface defects in metal products and identify the main problems of visual flaw detection. Identify areas for their improvement.
2. Analyze existing methods of image segmentation with image defect recognition. Justify the use of neural networks to improve the quality of defect detection and select the required type of network.
3. Develop algorithmic and software.
4. Conduct testing on real data and obtain quantitative estimates of the system's performance.
5. Analyze the system's performance using different backbones and different mask binarization thresholds.

Object of research: the process of visual and optical flaw detection of metal products.
Subject of research: methods of automated detection of metal defects using neural networks.

Scientific novelty: The algorithms for segmentation of images of surface defects of metal products have been improved by using the latest models of neural networks with deep learning, which has increased the reliability of automated defect detection.
Practical value: software for automated defect segmentation and software algorithms that automate this process and increase reliability have been developed. Recommendations for the selection of backbones and thresholds for mask binarization have been developed.

Research advisor: A. Protasov, A. Momot

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All Masters Thesis

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The Master's thesis consists of 4 chapters, 97 pages, contains 39 illustrations, 33 tables, 46 sources were processed.
Purpose of the work: automation of the process of recognition of cancer, which will increase the accuracy and reliability of diagnostic systems.
The object of research is tumor diseases.
The subject of research is neural network algorithms for detection and classification of diseases based on ultrasound images.

Tasks of the master's work:
1. To analyze existing diseases and methods of their detection. Analyze treatment algorithms in order to identify the right moment for diagnosis. To scrutinize all existing systems and find ways to improve them.

Hits: 101

The master's dissertation consists of 5 sections, 95 pages, contains 23 illustrations, 38 tables, was processed 38 sources.
Purpose of the work: automation process of analysis thermographic images using neural network technologies, which will increase the information content and reliability of thermal imaging video surveillance systems.

Tasks of the master's dissertation:
1. Analyze the current state of thermal imaging video surveillance systems and identify areas for their improvement.
2. To get acquainted with the existing methods automated detection and recognition of objects on thermographic images.
3. Justify use of neural networks to improve quality of object detection and select the required type of network.

Hits: 116

The master's thesis consists of 97 pages, 40 figures, 27 literary sources.
The master's thesis represents the formulation and solution of the problem of designing an acoustic-emission system for detecting cracks in a long, metal object of control. The task of this project is to calculate the piezo transducer and the electroacoustic paths of the sensor to control the leakage of liquids in the pipeline. The master's thesis contains calculations of: geometric dimensions of the control unit (piezoelectric transducer, location of sensors on the control object) taking into account control features, control probability and electrical elements. The graphic part of the master's thesis, the structural diagram of the sensor, made on a sheet of A3, a functional diagram – on a sheet of A2, a component drawing of the sensor - on a sheet of A1, an electrical schematic diagram - on a sheet of A0, and specifications for an electrical schematic diagram and a component drawing of the sensor.

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The main text of master's thesis consists of four sections and is laid out on 96 pages. In the course of materials work writing 48 sources of scientific literature were processed.
Actuality of the work: lung diseases are one of the significant causes of mortality worldwide. Every day, radiologists face the task of diagnosing lung diseases by analyzing X-ray images of the patient's chest. The development of machine learning algorithms provides wide opportunities in the field of automation of solving biomedical tasks. The possible application of computer processing of X-ray images will increase the accuracy of image analysis, reduce the role of the human factor in decision-making, allow to evaluate the effectiveness of the use of therapy, qualitatively and quickly classify data from the images and generally improve the quality of people's lives.

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  1. Melnyk A. Optimization of the signal processing algorithm for the ionizing radiation automated control system
  2. Kolomiiets K. Development of software for automated design of flow meters
  3. Klochko O. Intelligent system of automatic microsatellite control
  4. Morozov Y. The system of automated measurement of the angle of inclination

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АСНК КПІ ім. Ігоря Сікорського, 2021