Smart system for real time monitoring and diagnosis of dengue surfaces in Bangladesh

Abstract

Efficient vector management techniques hold great importance according to the World Health Organization (WHO) as a foundation to reduce and maintain the decline of vector-born diseases. Health surveillance teams operating in malaria and dengue and Zika and Chikungunya endemic areas can effectively use drone or unmanned aerial vehicles (UAVs) as technology to detect and eradicate mosquito breeding sites. UAVs enable users to obtain detailed aerial photographs and monitor locations throughout time and geographic areas. The process of vector control intervention analysis through manual image inspection requires extensive labor efforts and takes significant amounts of time. This research presents a methodology to automatically detect mosquito breeding areas in aerial drone images. Leveraging a CBAM-enhanced YOLOv9 object detection framework, we present a UAV-based strategy for dengue surface monitoring, achieving an impressive mean Average Precision (mAP) of 99.5% for mAP50, 86.4% for mAP50-95, 94% for Intersection over Union (IoU), 45 FPS, 98% precision, and 90% recall. The integration of the Convolutional Block Attention Module (CBAM) enhances the model’s feature extraction capabilities, improving its focus on critical regions in the images. Robust performance was ensured by the consistent achievement of these outcomes in a variety of operational and environmental contexts, including urban and rural locations. To confirm the model’s practicality, more tests under various circumstances will be carried out. This deep learning approach facilitates targeted and timely vector control interventions, leveraging drone-based surveillance to combat the spread of vector-borne diseases efficiently.