The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (3): 32-40.doi: 10.19682/j.cnki.1005-8885.2023.1001

• Artificial intelligence • Previous Articles     Next Articles

Data augmentation via joint multi-scale CNN and multi-channel attention for bumblebee image generation

Du Rong, Chen Shudong, Li Weiwei, Zhang Xueting, Wang Xianhui, Ge Jin   

  1. 1. Intelligent Manufacturing Electronics Research and Development Center, Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029, China  2. School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China 3. Institute of Zoology, Chinese Academy of Sciences, Beijing 100080, China  4. Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-05-20 Revised:2023-03-14 Online:2023-06-30 Published:2023-06-30
  • Contact: Chen Shudong


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.

Key words: data augmentation, image generation, attention mechanism

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