A RESEARCH OF THE LATEST APPROACHES TO VISUAL IMAGE RECOGNITION AND CLASSIFICATION
DOI:
https://doi.org/10.15588/1607-3274-2024-1-13Keywords:
machine learning, computer vision, image processing, convolutional neural networks, visual image recognition, visual image classification, algorithms, telecommunication systemsAbstract
Context. The paper provides an overview of current methods for recognizing and classifying visual images in static images or video stream. The paper will discuss various approaches, including machine learning, current problems of these methods and possible improvements. The biggest challenges of the visual image retrieval and classification task are discussed. The main emphasis is placed on the review of such promising algorithms as SSD, YOLO, R-CNN, an overview of the principles of these methods, network architectures.
Objective. The aim of the work is to analyze existing studies and find the best algorithm for recognizing and classifying visual images for further activities.
Method. Primary method is to compare different factors of algorithms in order to select the most perspective one. There are different marks to compare, like image processing speed, accuracy. There are a number of studies and publications that propose methods and algorithms for solving the problem of finding and classifying images in an image [3–6]. It should be noted that most promising approaches are based on machine learning methods. It is worth noting that the proposed methods have drawbacks due to the imperfect implementation of the Faster R-CNN, YOLO, SSD algorithms for working with streaming video. The impact of these drawbacks can be significantly reduced by applying the following solutions: development of combined identification methods, processing of edge cases – tracking the position of identified objects, using the difference between video frames, additional preliminary preparation of input data. Another major area for improvement is the optimization of methods to work with real-time video data, as most current methods focus on images.
Results. As an outcome of the current research we have found an optimal algorithm for further researches and optimizations.
Conclusions. Analysis of existent papers and researches has demonstrated the most promising algorithm for further optimizations and experiments. Also current approaches still have some space for further. The next step is to take the chosen algorithm and investigate possibilities to enhance it.
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Copyright (c) 2024 V. P. Lysechko, B. I. Sadovnykov, O. M. Komar, О. S. Zhuchenko
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