Deep learning for snake pattern detection

• Main Author: Ching, Jia Chin
• Other Authors: Owen Noel Newton Fernando
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
• Online Access: https://hdl.handle.net/10356/138044
• Institution: Nanyang Technological University

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.