Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area
This study developed scene recognition and cloud computing technology for real-time environmental image-based regional planning using artificial intelligence. TensorFlow object detection functions were used for artificial intelligence technology. First, an image from the environment is transmitted...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Taiwan Association of Engineering and Technology Innovation
2020-01-01
|
Series: | Advances in Technology Innovation |
Subjects: | |
Online Access: | http://ojs.imeti.org/index.php/AITI/article/view/4284 |
id |
doaj-2a897fe28acd4c91b842b3a9a219af02 |
---|---|
record_format |
Article |
spelling |
doaj-2a897fe28acd4c91b842b3a9a219af022020-11-25T02:09:58ZengTaiwan Association of Engineering and Technology InnovationAdvances in Technology Innovation2415-04362518-29942020-01-0151Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable AreaJia-Shing Sheu0Chen-Yin Han1Department of Computer Science, National Taipei University of Education, Taipei, TaiwanDepartment of Computer Science, National Taipei University of Education, Taipei, Taiwan This study developed scene recognition and cloud computing technology for real-time environmental image-based regional planning using artificial intelligence. TensorFlow object detection functions were used for artificial intelligence technology. First, an image from the environment is transmitted to a cloud server for cloud computing, and all objects in the image are marked using a bounding box method. Obstacle detection is performed using object detection, and the associated technique algorithm is used to mark walkable areas and relative coordinates. The results of this study provide a machine vision application combined with cloud computing and artificial intelligence scene recognition that can be used to complete walking space activities planned by a cleaning robot or unmanned vehicle through real-time utilization of images from the environment. http://ojs.imeti.org/index.php/AITI/article/view/4284TensorFlowcomputer visionneural networkscene recognitioncloud computing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jia-Shing Sheu Chen-Yin Han |
spellingShingle |
Jia-Shing Sheu Chen-Yin Han Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area Advances in Technology Innovation TensorFlow computer vision neural network scene recognition cloud computing |
author_facet |
Jia-Shing Sheu Chen-Yin Han |
author_sort |
Jia-Shing Sheu |
title |
Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area |
title_short |
Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area |
title_full |
Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area |
title_fullStr |
Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area |
title_full_unstemmed |
Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area |
title_sort |
combining cloud computing and artificial intelligence scene recognition in real-time environment image planning walkable area |
publisher |
Taiwan Association of Engineering and Technology Innovation |
series |
Advances in Technology Innovation |
issn |
2415-0436 2518-2994 |
publishDate |
2020-01-01 |
description |
This study developed scene recognition and cloud computing technology for real-time environmental image-based regional planning using artificial intelligence. TensorFlow object detection functions were used for artificial intelligence technology. First, an image from the environment is transmitted to a cloud server for cloud computing, and all objects in the image are marked using a bounding box method. Obstacle detection is performed using object detection, and the associated technique algorithm is used to mark walkable areas and relative coordinates. The results of this study provide a machine vision application combined with cloud computing and artificial intelligence scene recognition that can be used to complete walking space activities planned by a cleaning robot or unmanned vehicle through real-time utilization of images from the environment.
|
topic |
TensorFlow computer vision neural network scene recognition cloud computing |
url |
http://ojs.imeti.org/index.php/AITI/article/view/4284 |
work_keys_str_mv |
AT jiashingsheu combiningcloudcomputingandartificialintelligencescenerecognitioninrealtimeenvironmentimageplanningwalkablearea AT chenyinhan combiningcloudcomputingandartificialintelligencescenerecognitioninrealtimeenvironmentimageplanningwalkablearea |
_version_ |
1724921373035331584 |