Harnessing object detection for learning
The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we ne...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/77715 |
| _version_ | 1811688022656155648 |
|---|---|
| author | Yap, Jinson |
| author2 | Wang Han |
| author_facet | Wang Han Yap, Jinson |
| author_sort | Yap, Jinson |
| collection | NTU |
| description | The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we need to train. Object recognition for new object in datasets requires more time to process to those classifiers, as it needs to be trained to allow the database to increase. However, there are existing file that have datasets like TensorFlow. This project proposed to use this content to implement on app to enhance the children’s education through technology. Education is key to development in kids learning ability and with this project it will enhance the kids learning. This project labels each individual elements of an image into its own category regions and provide a label for each object. The of methods extracting features from an annotated image are store into database containing about 100000 images and 200 objects. Every parameter has its own futures that can be explored, and analysed to achieved the best accuracy. |
| first_indexed | 2024-10-01T05:25:36Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77715 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:25:36Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/777152023-07-07T16:06:55Z Harnessing object detection for learning Yap, Jinson Wang Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The current rapid development of technology and applications of object detection has always been an important Image recognition is a research area that is ongoing and is always challenging to task it in computer vision in many areas. There is a large array of different object categories, hence we need to train. Object recognition for new object in datasets requires more time to process to those classifiers, as it needs to be trained to allow the database to increase. However, there are existing file that have datasets like TensorFlow. This project proposed to use this content to implement on app to enhance the children’s education through technology. Education is key to development in kids learning ability and with this project it will enhance the kids learning. This project labels each individual elements of an image into its own category regions and provide a label for each object. The of methods extracting features from an annotated image are store into database containing about 100000 images and 200 objects. Every parameter has its own futures that can be explored, and analysed to achieved the best accuracy. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-04T05:56:11Z 2019-06-04T05:56:11Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77715 en Nanyang Technological University 52 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Yap, Jinson Harnessing object detection for learning |
| title | Harnessing object detection for learning |
| title_full | Harnessing object detection for learning |
| title_fullStr | Harnessing object detection for learning |
| title_full_unstemmed | Harnessing object detection for learning |
| title_short | Harnessing object detection for learning |
| title_sort | harnessing object detection for learning |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77715 |
| work_keys_str_mv | AT yapjinson harnessingobjectdetectionforlearning |