Object detection using machine learning techniques
Computer Vision is now an active research area under Deep Learning and Object Detection is an integral part of Computer Vision. Having a more complete idea of Deep Learning-based Object Detection Application, I start building up my own object detector for 4 classes of objects: person, car, bicycle...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78281 |
| _version_ | 1826117103367749632 |
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| author | Li, Ling |
| author2 | Huang Guangbin |
| author_facet | Huang Guangbin Li, Ling |
| author_sort | Li, Ling |
| collection | NTU |
| description | Computer Vision is now an active research area under Deep Learning and Object Detection is an integral part of Computer Vision.
Having a more complete idea of Deep Learning-based Object Detection Application, I start building up my own object detector for 4 classes of objects: person, car, bicycle and motorbike. This object detector is tailored for use in Singapore and I achieve a good mAP of 42% which is much higher than the official statistics provided by TensorFlow. In addition, I not only conduct inference test on my object detector model with image inputs, but also I test it with a live stream video by using Webcam. What’s more interesting is that I deploy this object detection model in a mobile device. It can simulate the driving scenario by realizing real-time object detection. |
| first_indexed | 2024-10-01T04:22:16Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78281 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:22:16Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/782812023-07-07T16:05:20Z Object detection using machine learning techniques Li, Ling Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Computer Vision is now an active research area under Deep Learning and Object Detection is an integral part of Computer Vision. Having a more complete idea of Deep Learning-based Object Detection Application, I start building up my own object detector for 4 classes of objects: person, car, bicycle and motorbike. This object detector is tailored for use in Singapore and I achieve a good mAP of 42% which is much higher than the official statistics provided by TensorFlow. In addition, I not only conduct inference test on my object detector model with image inputs, but also I test it with a live stream video by using Webcam. What’s more interesting is that I deploy this object detection model in a mobile device. It can simulate the driving scenario by realizing real-time object detection. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-14T06:41:24Z 2019-06-14T06:41:24Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78281 en Nanyang Technological University 71 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Li, Ling Object detection using machine learning techniques |
| title | Object detection using machine learning techniques |
| title_full | Object detection using machine learning techniques |
| title_fullStr | Object detection using machine learning techniques |
| title_full_unstemmed | Object detection using machine learning techniques |
| title_short | Object detection using machine learning techniques |
| title_sort | object detection using machine learning techniques |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/78281 |
| work_keys_str_mv | AT liling objectdetectionusingmachinelearningtechniques |