基于深度学习的伊蚊幼虫分类检测系统

Muhammad Izzul Azri Bin Zainol Azman, A. Sarlan
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引用次数: 4

摘要

如今,人工智能和镜头等最新技术的出现,可以捕捉像幼虫这样的微型生物,已经在我们周围的环境中使用。深度学习技术是人工智能的一个子集,已被用于处理图像。与此之前一样,有一项研究是利用物联网(IoT)技术检测某些地方的湿度,并将其与伊蚊孳生的可能性联系起来,以检测伊蚊孳生的可能场所。为支持研究并验证该地点为伊蚊孳生地,提出了对伊蚊幼虫进行分类和检测的研究。利用深度学习的伊蚊幼虫分类和检测系统(ALCD)是利用深度学习技术检测幼虫的模式并根据其类型进行分类的系统。由于马来西亚全年登革热病例迅速增加,因此建议开发该系统。虽然政府和非政府组织(ngo)有许多方法来帮助抗击登革热病毒的爆发,但这项研究的重点是在早期阶段防止病毒的传播。伊蚊的生命周期是从卵到幼虫到蛹,最后成为成虫。可用于区分伊蚊和非伊蚊的伊蚊早期阶段为幼虫期。本研究旨在利用人工智能技术的深度学习子集的最新技术来寻找伊蚊和非伊蚊在幼虫上的模式进行背景研究。识别幼虫类型模式后,可继续进行伊蚊幼虫与非伊蚊幼虫的分类。将进行实时分类测试,以测试ALCD系统的准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aedes Larvae Classification and Detection (ALCD) System by Using Deep Learning
Nowadays, the presence of the latest technologies like Artificial Intelligence and lenses that can capture the micro-living being like larva have been used in our surrounding environment. Deep Learning technologies which are a subset of Artificial Intelligence have been implemented in used for processing the image. As before this, there is a study to detect the possible place of Aedes mosquito breeding place with the use of Internet of Things (IoT) technologies to detect the humidity of certain places and relate it to the possibility of Aedes mosquito breeding present. To support the study and have verification of the place is the breeding place of Aedes mosquito, a study to classify the larva and detect it has been proposed. The Aedes Larvae Classification and Detection (ALCD) System by using Deep learning is a system that uses deep learning technologies to detect the pattern of the larva and classify it according to its type. The proposed developed system ALCD because Malaysia is having a rapid increase in dengue cases throughout the year. While there are many approaches from the government and non-government organizations (NGOs) to help combat the dengue virus outbreak, this study is focusing on preventing the virus from spreading in the early stages. The life cycle of an Aedes mosquito is starting from the egg to larva to pupa and lastly became an adult mosquito. The early stages of Aedes mosquito that can be used to differentiate between Aedes and Non-Aedes were at the larva stages. This study is meant to do a background study on using the latest technology of deep learning subset of Artificial Intelligence technology to find the pattern of the Aedes and Non-Aedes on the larva. After the pattern of the larva type is recognized, the process to classify it between the Aedes larvae and Non-Aedes larvae can be continued for classification. Real-time classification testing will be conducted to test the accuracy and efficiency of the ALCD system.
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