title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÐºÐ»Ð°ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ Ð¿Ð¾Ð´Ñ Ð¾Ð´Ð¾Ð² Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð¼ Ð·ÑÐµÐ½Ð¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research and comparison of methods for detecting objects in images using classical and neural approaches in machine vision
Creators: name: ÐÐ¾Ð½Ð¾Ð², Ð Ð¾Ð¼Ð°Ð½
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: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
R-CNN
YOLO
MATLAB
OpenCV
machine vision
neural networks
deep learning

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description: The work is devoted to the study of modern methods of object detection using the example of solving the problem of detecting products in conveyor production, developing software implementations of object detection using the classical and neural methods, and drawing conclusions about the advantage of one of the methods. The research set the following goals: â¢ Research of classical object detection methods, image preprocessing methods and algorithms for direct detection of object boundaries. â¢ Research of object detection methods using neural networks, neural network architectures developed directly for object search and classification. â¢ Search and selection of tools necessary for the implementation of technical solutions of both methods. â¢ Implementation of both detection methods and demonstration of their operation. â¢ Analysis of both methods, conclusion about the best method for this task. In the course of the work, the principles of classical and neural approaches in object detection are investigated. The most suitable solutions for both methods have been selected. A neural network model for object detection and a cross-platform algorithm for using a trained neural network have been developed and trained. A cross-platform method for detecting objects using classical methods has been developed. A demo application has been developed. The result of the research shows that the neural method has greater stability, better detection accuracy, does not require additional adjustment when changing lighting or changing the scene behind the object, however, it requires much more calculations than classical methods.