Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine
Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine
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DOI：
10.3390/app7010051
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发表时间：
2017-01-01
影响因子：
2.7
通讯作者：
Nakamura, Akio
中科院分区：
文献类型：
--
作者：
Fuchida, Masataka;Pathmakumar, Thejus;Nakamura, Akio
近年来，随着蚊媒疾病和相关伤亡人数的急剧增加，对新型自动蚊子感知和分类方法的需求变得越来越重要。有遥感和GIS为基础的方法来映射潜在的蚊子居民和地点，容易发生蚊媒疾病，但这些方法通常不占物种明智的识别蚊子在封闭的周边地区。传统的蚊子分类方法涉及高度人工的过程，需要繁琐的样本收集和监督实验室分析。在这项研究工作中，我们提出了一个自动化的基于视觉的蚊子分类模块，可以部署在封闭的周边蚊子居民的设计和实验验证。该模块能够通过提取形态特征，然后基于支持向量机的分类来识别蚊子和其他昆虫，如蜜蜂和苍蝇。此外，本文提出了三种变体的支持向量机分类器的蚊子分类问题的背景下的结果。这种基于视觉的蚊子分类方法为蚊子监测、地图绘制和样本图像收集提供了一种有效的替代方法。实验结果涉及蚊子和一组预定义的其他错误使用多种分类策略之间的分类证明了所提出的方法的有效性和有效性，最大召回率为98%。
The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.