Mosquito swarm counting via attention-based multi-scale convolutional neural network

Abstract

Monitoring mosquito density to predict the risk of transmission of the virus and develop a response in advance is an important part of prevention efforts. This paper aims to estimate accurately the mosquito swarm count from a given image. To this end, we proposed an attention-based multi-scale mosquito swarm counting model that consists of the feature extraction network (FEN) and attention based multi-scale regression network (AMRN). The FEN uses VGG-16 network to extract low-level features of mosquitoes. The AMRN adopts a multi-scale convolutional neural network, and with a squeeze and excitation channel attention module in the branch with a 7 × 7 convolution kernel to extract high-level features, map the feature map to the mosquito swarm density map and estimate mosquitoes count. We collected and labelled a data set that includes 391 mosquito swarm images with 15,466 mosquitoes. Experiments show that our method performs well on the data set and achieves mean absolute error (MAE) of 1.810 and root mean square error (RMSE) of 3.467.