| I&T Solution |
Self-sustained and AI-driven Railway Intrusion Object Detection System
(REF: S-0973) |
| Trial Project |
|
| Solution Feature |
- Standalone wire-free visual sensor with built-in a 4G LTE network powered by the solar panel and rechargeable battery
- An edge AI computing device powered by a portable standalone solar-battery system
- A computer vision AI software for automated real-time railway intrusion object detection
- A self-adaptive federated learning framework for cloud-edge updates of AI models in the software
- A cloud hosted analytical system for generating alarms of railway intrusion object detection
|
| Trial Application and Expected Outcome |
- Develop and setup the standalone visual sensor and AI computing device as well as make sure they are able to self-sustain for 24/7
- Test and make-sure the data (captured video of the targeted railway scene) streaming from the visual sensor to the local AI computing device via wireless transmission
- Implement the computer vision based railway intrusion object detection software and report the performance evaluation
- Implement the cloud-edge coordinated analytics and alarming system as well as provide the final project demo
|
| Additional Solution Information |
Project Details.pptx
|
| Info on I&T Solution Provider |
| Solution Provider | : | Hong Kong Institute for Data Science | | Address | : | P7311, Yeung Kin Man Academic Building, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong | | Contact Person | : | Zijun Zhang |
| Position | : | Associate Professor | | Tel | : | 34425328 | | Email | : |
zijzhang@cityu.edu.hk |
|
