Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking
Keeping track of household inventory can be a time-consuming and tedious task. Having passed by the supermarket and you have no idea which groceries/items at home are running low and required to be topped up. What if you have “eyes” inside your cabinet to tell you the information that you need? This...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166850 |
| _version_ | 1826122785344192512 |
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| author | Ong, Qiu Xi |
| author2 | Chong Yong Kim |
| author_facet | Chong Yong Kim Ong, Qiu Xi |
| author_sort | Ong, Qiu Xi |
| collection | NTU |
| description | Keeping track of household inventory can be a time-consuming and tedious task. Having passed by the supermarket and you have no idea which groceries/items at home are running low and required to be topped up. What if you have “eyes” inside your cabinet to tell you the information that you need? This is very much practical and convenient in our daily lives thanks to modern technologies. Millions of people's lives could be improved by this by easing the stress of keeping track of household goods and freeing up time for more pleasurable pursuits.
The objective of this project is to develop an object-detecting inventory tracking system with the Raspberry Pi 3 model. The system uses a camera to capture images of household items and applies YOLOv4 tiny object detection to identify the objects in the images. Having identified the objects, the system will be able to quantify the objects in each category. The inventory tracking will be updated periodically to the Microsoft Azure IoT hub, and users will be able to access the data remotely when they need it. |
| first_indexed | 2024-10-01T05:53:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166850 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:53:53Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1668502023-07-07T17:36:57Z Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking Ong, Qiu Xi Chong Yong Kim School of Electrical and Electronic Engineering EYKCHONG@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems Keeping track of household inventory can be a time-consuming and tedious task. Having passed by the supermarket and you have no idea which groceries/items at home are running low and required to be topped up. What if you have “eyes” inside your cabinet to tell you the information that you need? This is very much practical and convenient in our daily lives thanks to modern technologies. Millions of people's lives could be improved by this by easing the stress of keeping track of household goods and freeing up time for more pleasurable pursuits. The objective of this project is to develop an object-detecting inventory tracking system with the Raspberry Pi 3 model. The system uses a camera to capture images of household items and applies YOLOv4 tiny object detection to identify the objects in the images. Having identified the objects, the system will be able to quantify the objects in each category. The inventory tracking will be updated periodically to the Microsoft Azure IoT hub, and users will be able to access the data remotely when they need it. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-15T02:29:58Z 2023-05-15T02:29:58Z 2023 Final Year Project (FYP) Ong, Q. X. (2023). Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166850 https://hdl.handle.net/10356/166850 en P3048-212 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Ong, Qiu Xi Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title | Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title_full | Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title_fullStr | Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title_full_unstemmed | Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title_short | Embedded system application development on Raspberry Pi 3 - machine learning household inventory tracking |
| title_sort | embedded system application development on raspberry pi 3 machine learning household inventory tracking |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/166850 |
| work_keys_str_mv | AT ongqiuxi embeddedsystemapplicationdevelopmentonraspberrypi3machinelearninghouseholdinventorytracking |