由於登革熱與日本腦炎是由病毒所引起的一種傳染病，會經由蚊子傳播給人類。在最近一次 2015 年的台南市爆發登革熱的疫情，最初只出現在台南市北部地區，接著以驚人的速度擴散到全台南市，最終蔓延至台灣全島。當年，確診病例超過 4 萬人，死亡病例也高達 218 人，而且未發病的感染者約為發病者的九倍至十倍。若患者再次被病媒蚊叮咬造成交叉感染，則重症死亡率會大幅度提升至 20%以上，而且目前沒有預防疫苗，也沒有特效藥物可治療，而引發登革熱的病媒蚊為埃及斑蚊（Aedes aegypti）與白線斑蚊 (Aedes albopictus)。而日本腦炎的致死率大約為 20%以上，存活病例約有 40%有神經性或精神性的後遺症，而且亦目前沒有特效藥可治療，引發日本腦炎的病媒蚊為三斑家蚊（Culex tritaeniorhynchus）與環蚊家蚊（Culex annulus），避免病媒蚊叮咬是目前唯一的預防登革熱及日本腦炎的方法。
為解決登革熱與日本腦炎問題，本篇論文提出病媒蚊分類系統，這是一套影像分類準確率高達 98%以及計數功能的智慧捕蚊系統，其中包含邊緣計算、深度學習的影像處理和 電腦視覺，主要功能在邊緣計算為物體偵測，深度學習為斑蚊分類與計數，透過這些步驟，改善了現今捕蚊燈、滅蚊燈不能分類 (Classification)蚊子種類。並以智慧捕蚊裝置收集影像資料，主要資料收集與處理正是引發登革熱的兩種台灣常見的病媒蚊種類──白線斑蚊與埃及斑蚊以及引發日本腦炎的兩種台灣常見的病媒蚊種類──三斑家蚊與環蚊家蚊，並在分類時以斑蚊 (Aedes) 和家蚊 (Culex) 進行二元分類，由於此系統與裝置獲得更多台灣蚊子資訊，其資訊包含進入捕蚊燈的蚊子數量、種類以及時間、地點，以便後續作為對病媒蚊採取措施的重要參考依據。

Dengue fever and Japanese encephalitis are mosquito-borne diseases which are infectious diseases caused by viruses, they. It is particularly dangerous for children and can lead to death, less than 1 percent of cases cause fatalities even with proper medical care, according to the World Health Organization (WHO). However, dengue fever symptoms which may include a high fever, headache joint pains and muscle, and a skin rash. typically begin three days to two weeks after infection. In the most recent outbreak of dengue fever in Tainan City, Taiwan in 2015. It first appeared only in the northern part of Tainan City, then it spread to all over Tainan City at an alarming rate ,and eventually it spread to all over whole Taiwan islands. In that year, the number of confirmed cases exceeded 40,000, and the number of death reached 218. Actually, there are no vaccines for prevention and there are no specific drugs for treatment. However, the mosquitoes that cause dengue fever are Aedes aegypti and Aedes albopictus, and the mosquito that causes Japanese encephalitis are Culex tritaeniorhynchus and Culex annulus. The most importantly, avoiding vector mosquitoes bites is the only way to prevent dengue fever and Japanese encephalitis. In order to alleviate the problem of dengue fever and Japanese encephalitis, this paper proposes the vector mosquitoes classification system. This system is a intelligent mosquitocatching system with image classification accuracy of up to 98%. This system includes Edge Computing, Deep Learning Image Processing, and Computer Vision to improve the problem of classification in mosquito traps and mosquito killer lamp. The main data collection and processing are the two species of mosquitoes common in Taiwan causing Dengue fever: Aedes albopictus and Aedes aegypti, and two types of Taiwanese common mosquitoes that cause Japanese encephalitis. Culex tritaeniorhynchus and Culex annulus. In this paper, Aedes and Culex were used for binary classification. This system and device will obtain more information on mosquitoes in Taiwan, the information includes the number , type and time and place of vector mosquitoes. This can provide important information to take measures against vector mosquitoes.

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