The difficulty of bumblebee data collecting and the laborious nature of bumblebee data annotation sometimes result in a lack of training data, which impairs the effectiveness of deep learning based counting methods. Given that it is challenging to produce the detailed background information in the generated bumblebee images using current data augmentation methods, in this paper, a joint multi-scale convolutional neural network and multi-channel attention based generative adversarial network (MMGAN) is proposed. MMGAN generates the bumblebee image in accordance with the corresponding density map marking the bumblebee positions. Specifically, the multi-scale convolutional neural network ( CNN) module utilizes multiple convolution kernels to completely extract features of different scales from the input bumblebee image and density map. To generate various targets in the generated image, the multi-channel attention module builds numerous intermediate generation layers and attention maps. These targets are then stacked to produce a bumblebee image with a specific number of bumblebees. The proposed model obtains the greatest performance in bumblebee image generating tasks, and such generated bumblebee images considerably improve the efficiency of deep learning based counting methods in bumblebee counting applications.
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2023.1001