Artificial Intelligence Application Open Platform for Rail Transit
Insufficient data, lack of expertise in AI application development, and weak device computing capabilities have severely restricted the rapid engineering implementation of rail transit intelligent products. In order to solve these problems, this paper proposes an AI open platform for rail transit. I...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2022-02-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.200 |
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| _version_ | 1849224965886312448 |
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| author | LIN Jun LIU Yue WANG Quandong YOU Jun DING Chi LIU Ren |
| author_facet | LIN Jun LIU Yue WANG Quandong YOU Jun DING Chi LIU Ren |
| author_sort | LIN Jun |
| collection | DOAJ |
| description | Insufficient data, lack of expertise in AI application development, and weak device computing capabilities have severely restricted the rapid engineering implementation of rail transit intelligent products. In order to solve these problems, this paper proposes an AI open platform for rail transit. It builds connection between model training, edge computing and cloud-edge collaboration, and provides full-process AI application development solutions. In the cloud, this platform builds an AI development tool chain including data annotation, algorithm design, model training and application generation. It also provides an efficient model inference framework at the edge. Data collection and model deployment are implemented through the cloud-edge collaboration mechanism. Since unmanned mining trucks use autonomous driving technology similar to rail transit, this paper takes the visual perception application of unmanned mining trucks as an example for verification. The result shows that using AI open platform for application development can effectively reduce application development and deployment time, from 3~4 months normal period to present 1 month.The mean average precision of the visual detection model for stones, mining truck and other targets reaches 0.988, achieving excellent perceptual performance. |
| format | Article |
| id | doaj-art-9498070533c2457fbd4d627e4b0d9c44 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-9498070533c2457fbd4d627e4b0d9c442025-08-25T06:48:51ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272022-02-01647023090962Artificial Intelligence Application Open Platform for Rail TransitLIN JunLIU YueWANG QuandongYOU JunDING ChiLIU RenInsufficient data, lack of expertise in AI application development, and weak device computing capabilities have severely restricted the rapid engineering implementation of rail transit intelligent products. In order to solve these problems, this paper proposes an AI open platform for rail transit. It builds connection between model training, edge computing and cloud-edge collaboration, and provides full-process AI application development solutions. In the cloud, this platform builds an AI development tool chain including data annotation, algorithm design, model training and application generation. It also provides an efficient model inference framework at the edge. Data collection and model deployment are implemented through the cloud-edge collaboration mechanism. Since unmanned mining trucks use autonomous driving technology similar to rail transit, this paper takes the visual perception application of unmanned mining trucks as an example for verification. The result shows that using AI open platform for application development can effectively reduce application development and deployment time, from 3~4 months normal period to present 1 month.The mean average precision of the visual detection model for stones, mining truck and other targets reaches 0.988, achieving excellent perceptual performance.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.200edge computingmode trainingcloud-edge collaborationAI application open platformvisual perception |
| spellingShingle | LIN Jun LIU Yue WANG Quandong YOU Jun DING Chi LIU Ren Artificial Intelligence Application Open Platform for Rail Transit Kongzhi Yu Xinxi Jishu edge computing mode training cloud-edge collaboration AI application open platform visual perception |
| title | Artificial Intelligence Application Open Platform for Rail Transit |
| title_full | Artificial Intelligence Application Open Platform for Rail Transit |
| title_fullStr | Artificial Intelligence Application Open Platform for Rail Transit |
| title_full_unstemmed | Artificial Intelligence Application Open Platform for Rail Transit |
| title_short | Artificial Intelligence Application Open Platform for Rail Transit |
| title_sort | artificial intelligence application open platform for rail transit |
| topic | edge computing mode training cloud-edge collaboration AI application open platform visual perception |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.200 |
| work_keys_str_mv | AT linjun artificialintelligenceapplicationopenplatformforrailtransit AT liuyue artificialintelligenceapplicationopenplatformforrailtransit AT wangquandong artificialintelligenceapplicationopenplatformforrailtransit AT youjun artificialintelligenceapplicationopenplatformforrailtransit AT dingchi artificialintelligenceapplicationopenplatformforrailtransit AT liuren artificialintelligenceapplicationopenplatformforrailtransit |