Application of YOLOv7 for real-time detection of Aedes Aegypti

Danilo Machado de Oliveira, Samuel Mafra, Felipe Augusto Pereira de Figueiredo, Guilherme Pires Pieadade, Mateus R Cruz, Eduardo Henrique Teixeira

DOI: 10.14209/sbrt.2023.1570923269

Evento: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023)

Keywords: IoT LoRa/LoRaWan Dengue Computer Vision

Abstract

Public health faces significant challenges in combating the Aedes aegypti mosquito, which still threatens Brazilian population. Despite awareness campaigns and control measures, the incidence of diseases such as dengue, zika and chikungunya is still high. However, technological advances have enabled the development of devices capable of detecting Aedes aegypti female mosquitos, the main transmission vector of these diseases. The use of IoT (Internet of Things) systems and weather stations can help researchers in controlling the population of these insects, enabling the monitoring of high-risk areas. This article presents an intelligent system applying Computer Vision to detect Aedes aegypti mosquitos. The objective of this article is to present an IoT system architecture using the You Only Look Once v7 (YOLOv7) algorithm, which provides a better solution compared to others existing methods for real-time Aedes aegypti detection. By combining YOLOv7's real-time detection capabilities with the connectivity and intelligence of the IoT system, the proposed solution offers a significant advantage. Furthermore, the integration of the IoT architecture allows for continuous data collection and the implementation of advanced analytics, such as machine learning (ML), enabling improvements in the accuracy and efficiency of the detection system. This adaptive approach, joint the real-time responsiveness, makes the proposed solution highly effective and promising in combating Aedes aegypti.

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