Available parking spaces detection with deep learning
Image semantic segmentation has made great process with deep learning in recent years. There are various applications that need efficient and accurate segmentation systems. Image semantic segmentation has been widely used in modern medicine, robotics, and especially in the autonomous vehicle systems...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/10356/75358 |
| _version_ | 1826115590708789248 |
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| author | Li, Xiaochen |
| author2 | Zhou Bin |
| author_facet | Zhou Bin Li, Xiaochen |
| author_sort | Li, Xiaochen |
| collection | NTU |
| description | Image semantic segmentation has made great process with deep learning in recent years. There are various applications that need efficient and accurate segmentation systems. Image semantic segmentation has been widely used in modern medicine, robotics, and especially in the autonomous vehicle systems. An autonomous vehicle is a smart car that can sensing its surrounding environment and driving without human input, it is able to analysis the sensory data so that it can distinguish different objects such as cars and pedestrians on the road. With the great development and improvement of the Artificial Intelligence, we believe that automated driving will happen in the near future, and autonomous parking is a crucial step towards future autonomous driving. This report will give a study of the deep learning algorithms for image semantic segmentation of available parking spaces and CARLA Simulator. It will include a review of the basic principle, structure and design of the deep learning approach for image semantic segmentation, known as the Fully Convolutional Neural Networks model. Then it will show the training of the network and the results of the detection. The conclusion and the future works will be given in the end of the report. |
| first_indexed | 2024-10-01T03:57:39Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/75358 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:57:39Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/753582023-07-07T15:56:09Z Available parking spaces detection with deep learning Li, Xiaochen Zhou Bin Sugiri Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Image semantic segmentation has made great process with deep learning in recent years. There are various applications that need efficient and accurate segmentation systems. Image semantic segmentation has been widely used in modern medicine, robotics, and especially in the autonomous vehicle systems. An autonomous vehicle is a smart car that can sensing its surrounding environment and driving without human input, it is able to analysis the sensory data so that it can distinguish different objects such as cars and pedestrians on the road. With the great development and improvement of the Artificial Intelligence, we believe that automated driving will happen in the near future, and autonomous parking is a crucial step towards future autonomous driving. This report will give a study of the deep learning algorithms for image semantic segmentation of available parking spaces and CARLA Simulator. It will include a review of the basic principle, structure and design of the deep learning approach for image semantic segmentation, known as the Fully Convolutional Neural Networks model. Then it will show the training of the network and the results of the detection. The conclusion and the future works will be given in the end of the report. Bachelor of Engineering 2018-05-31T02:03:43Z 2018-05-31T02:03:43Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75358 en Nanyang Technological University 48 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Li, Xiaochen Available parking spaces detection with deep learning |
| title | Available parking spaces detection with deep learning |
| title_full | Available parking spaces detection with deep learning |
| title_fullStr | Available parking spaces detection with deep learning |
| title_full_unstemmed | Available parking spaces detection with deep learning |
| title_short | Available parking spaces detection with deep learning |
| title_sort | available parking spaces detection with deep learning |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/75358 |
| work_keys_str_mv | AT lixiaochen availableparkingspacesdetectionwithdeeplearning |