Aedes Aegypti larvae detection and tracking in turbid videos by semantic segmentation using deep learning

Abstract

Dengue mosquitoes are the only reason for dengue fever. To effectively combat this disease, it is important to eliminate dengue mosquitoes and their larvae. However, there are currently very few computer-aided models available in scientific literature to prevent the spread of dengue fever. Detecting the larvae stage of the dengue mosquito is particularly important in controlling its population. To address this issue, we propose an automated method that utilizes deep learning for semantic segmentation to detect and track dengue larvae. Our approach incorporates a contrast enhancement approach into the semantic neural network to make the detection more accurate. As there was no dengue larvae dataset available, we develop our own dataset having 50 short videos with different backgrounds and textures. The results show that the proposed model achieves up to 79% F-measure score. In comparison, the DeepLabV3, Resnet achieves up to 77%, and Segnet achieves up to 76% F-measure score on the tested frames. The results show that the proposed model performs well for small object detection and segmentation. The average F-measure score of all the frames also indicates that the proposed model achieves a 76.72% F-measure score while DeepLabV3 achieves a 75.37%, Resnet 75.41%, and Segnet 74.87% F-measure score.