Deep learning for snake pattern detection
Snakebites are a serious concern for many countries worldwide, especially for rural undeveloped countries. From snakebites alone, about a 100,000 people die every year in these countries and 3 times as many people experience lasting effects such as amputation and kidney failures. Our project, SnakeA...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138044 |
| _version_ | 1824453916775940096 |
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| author | Ching, Jia Chin |
| author2 | Owen Noel Newton Fernando |
| author_facet | Owen Noel Newton Fernando Ching, Jia Chin |
| author_sort | Ching, Jia Chin |
| collection | NTU |
| description | Snakebites are a serious concern for many countries worldwide, especially for rural undeveloped countries. From snakebites alone, about a 100,000 people die every year in these countries and 3 times as many people experience lasting effects such as amputation and kidney failures. Our project, SnakeAlert, goal is to reduce snakebites and raise public awareness. This year, we focus on improving snakebites response times via early snake recognition. We shall use image recognition to quickly identify venomous snakes and direct victims to the nearest hospital containing the required antivenom.
We used neural networks and machine learning techniques to train an A.I. to identify venomous snakes and achieved a 60% success rate at identify venomous snakes. This is a relatively high success rate & proves that image recognition technology can be applied to life saving snake recognition procedures. Furthermore, this technique is not yet optimised as it can be improved with a better dataset & neural network model. |
| first_indexed | 2025-02-19T03:14:01Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138044 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:14:01Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1380442020-04-22T09:09:31Z Deep learning for snake pattern detection Ching, Jia Chin Owen Noel Newton Fernando School of Computer Science and Engineering OFernando@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Snakebites are a serious concern for many countries worldwide, especially for rural undeveloped countries. From snakebites alone, about a 100,000 people die every year in these countries and 3 times as many people experience lasting effects such as amputation and kidney failures. Our project, SnakeAlert, goal is to reduce snakebites and raise public awareness. This year, we focus on improving snakebites response times via early snake recognition. We shall use image recognition to quickly identify venomous snakes and direct victims to the nearest hospital containing the required antivenom. We used neural networks and machine learning techniques to train an A.I. to identify venomous snakes and achieved a 60% success rate at identify venomous snakes. This is a relatively high success rate & proves that image recognition technology can be applied to life saving snake recognition procedures. Furthermore, this technique is not yet optimised as it can be improved with a better dataset & neural network model. Bachelor of Engineering (Computer Science) 2020-04-22T07:50:15Z 2020-04-22T07:50:15Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138044 en SCSE19-0170 application/vnd.ms-powerpoint application/pdf text/html Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Ching, Jia Chin Deep learning for snake pattern detection |
| title | Deep learning for snake pattern detection |
| title_full | Deep learning for snake pattern detection |
| title_fullStr | Deep learning for snake pattern detection |
| title_full_unstemmed | Deep learning for snake pattern detection |
| title_short | Deep learning for snake pattern detection |
| title_sort | deep learning for snake pattern detection |
| topic | Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | https://hdl.handle.net/10356/138044 |
| work_keys_str_mv | AT chingjiachin deeplearningforsnakepatterndetection |