Paper #2

A Deep Learning-based Dengue Mosquito Detection Method Using Faster R-CNN and Image Processing Techniques

Rumali Siddiqua, Shakila Rahman and Jia Uddin

Abstract: Dengue fever, a mosquito-borne disease caused by dengue viruses, is a significant public health concern in many countries especially in the tropical and subtropical regions. In this paper, we introduce a deep learning-based model using Faster R-CNN with InceptionV2 accompanied by image processing techniques to identify the dengue mosquitoes. Performance of the proposed model is evaluated using a custom mosquito dataset built upon varying environments which are collected from the internet. The proposed Faster R-CNN with InceptionV2 model is compared with other two state-of-art models, R-FCN with ResNet 101 and SSD with MobilenetV2. The False positive (FP), False negative (FN), precision and recall are used as performance measurement tools to evaluate the detection accuracy of the proposed model. The experimental results demonstrate that as a classifier the Faster- RCNN model shows 95.19% of accuracy and outperforms other state-of-the-art models as R-FCN and SSD model show 94.20% and 92.55% detection accuracy, respectively for the test dataset.

Keywords: Deep Learning; Dengue Mosquito; Detection Algorithms; Faster R-CNN; Image Processing; InceptionV2.