Recognition Road Scene Elements by Yudin Dmitry (6 results)

Paperback. Condition: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Taschenbuch. Condition: Neu. Recognition of road scene elements using deep neural networks | Monograph | Dmitry Yudin | Taschenbuch | 96 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139886869 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The monograph presents modern approaches to the analysis of the road environment images based on deep learning. The neural network architectures for solving problems of classification, segmentation of images, detection of objects on them are considered. Author analyzes in detail the deep neural network architectures for the detection of the road scene elements (vehicles, traffic lights) on the images from the on-board video camera. A solution is proposed for calculation of founded vehicle position. The approach to the use of convolutional neural networks of different architectures has been analyzed to detect the visibility loss of a video camera on the basis of recognition of images obtained from it. Author describes tools for creating datasets in the traffic scene recognition tasks. Author also presents examples of software and hardware implementation for these architectures using a graphics processor and NVidia CUDA technology. The publication is intended for scientists and engineers engaged in the development of machine vision systems using deep convolutional neural networks and may be useful for lecturers, students and postgraduates of relevant university specialties. 96 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yudin DmitryDmitry Yudin, PhD, associate professor of Technical cybernetics department of Belgorod state technological university named after V.G. Shukhov, Belgorod, winner of grant competition of the President of the Russian Federa.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The monograph presents modern approaches to the analysis of the road environment images based on deep learning. The neural network architectures for solving problems of classification, segmentation of images, detection of objects on them are considered. Author analyzes in detail the deep neural network architectures for the detection of the road scene elements (vehicles, traffic lights) on the images from the on-board video camera. A solution is proposed for calculation of founded vehicle position. The approach to the use of convolutional neural networks of different architectures has been analyzed to detect the visibility loss of a video camera on the basis of recognition of images obtained from it. Author describes tools for creating datasets in the traffic scene recognition tasks. Author also presents examples of software and hardware implementation for these architectures using a graphics processor and NVidia CUDA technology. The publication is intended for scientists and engineers engaged in the development of machine vision systems using deep convolutional neural networks and may be useful for lecturers, students and postgraduates of relevant university specialties.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The monograph presents modern approaches to the analysis of the road environment images based on deep learning. The neural network architectures for solving problems of classification, segmentation of images, detection of objects on them are considered. Author analyzes in detail the deep neural network architectures for the detection of the road scene elements (vehicles, traffic lights) on the images from the on-board video camera. A solution is proposed for calculation of founded vehicle position. The approach to the use of convolutional neural networks of different architectures has been analyzed to detect the visibility loss of a video camera on the basis of recognition of images obtained from it. Author describes tools for creating datasets in the traffic scene recognition tasks. Author also presents examples of software and hardware implementation for these architectures using a graphics processor and NVidia CUDA technology. The publication is intended for scientists and engineers engaged in the development of machine vision systems using deep convolutional neural networks and may be useful for lecturers, students and postgraduates of relevant university specialties.