Vladimir Soloviev | Московская академия экономики и права, Финансовый университет при правительстве Рф (original) (raw)
Papers by Vladimir Soloviev
Studies in Systems, Decision and Control, 2021
Symmetry, 2022
Despite the great possibilities of modern neural network architectures concerning the problems of... more Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric...
Advances in systems science and applications, 2018
This topic is of high relevance due to the fact that many currently available mathematical market... more This topic is of high relevance due to the fact that many currently available mathematical market risk assessment models contain many limitations for their effective use. However, these limitations are often not feasible, what leads to a decrease in forecast accuracy. To avoid this, more accurate models are necessary. Neural network-based models can show a more precise result due to their basic property – nonlinearity. The interest in neural networks re-emerged only after some important theoretical results were attained in the early eighties and new hardware developments increased the processing capacities. Artificial neural networks can be most adequately characterised as «computational models» with particular properties such as the ability to adapt or learn, to generalise, or to cluster or organise data, and which operation is based on parallel processing. The task of this paper is to build a model that can enable us to assess a market risk for a company. The primary goal of this...
Agronomy, 2020
A machine vision system for detecting apples in orchards was developed. The system was designed t... more A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to be used in an apple-harvesting robot machine vision system, providing an average apple detection time of 19 ms with a share of objects being mistaken for apples at 7.8% and a share of unrecognized apples at 9.2%. Both the average detection time and error rates are less than in all known similar systems. The system can operate not only in apple-harvesting robots but also in orange-harvesting robots.
Diversity, Divergence, Dialogue, 2021
Studies in Systems, Decision and Control, 2021
Symmetry, 2022
Despite the great possibilities of modern neural network architectures concerning the problems of... more Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric...
Advances in systems science and applications, 2018
This topic is of high relevance due to the fact that many currently available mathematical market... more This topic is of high relevance due to the fact that many currently available mathematical market risk assessment models contain many limitations for their effective use. However, these limitations are often not feasible, what leads to a decrease in forecast accuracy. To avoid this, more accurate models are necessary. Neural network-based models can show a more precise result due to their basic property – nonlinearity. The interest in neural networks re-emerged only after some important theoretical results were attained in the early eighties and new hardware developments increased the processing capacities. Artificial neural networks can be most adequately characterised as «computational models» with particular properties such as the ability to adapt or learn, to generalise, or to cluster or organise data, and which operation is based on parallel processing. The task of this paper is to build a model that can enable us to assess a market risk for a company. The primary goal of this...
Agronomy, 2020
A machine vision system for detecting apples in orchards was developed. The system was designed t... more A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to be used in an apple-harvesting robot machine vision system, providing an average apple detection time of 19 ms with a share of objects being mistaken for apples at 7.8% and a share of unrecognized apples at 9.2%. Both the average detection time and error rates are less than in all known similar systems. The system can operate not only in apple-harvesting robots but also in orange-harvesting robots.
Diversity, Divergence, Dialogue, 2021