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Investigations of butterfly species identification from images in natural environments

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Abstract

It has been a challenging problem to identify species of butterflies, especially from images taken in natural environments. Therefore the First international butterfly species recognition competition was organized at the third Data Mining Competition in China in 2018, so as to find good solutions to this challenging problem. The baseline for the competition was based on the Faster R-CNN for it was the latest deep learning algorithm at that time. Nearly all the competition teams chose the Faster R-CNN, or its variations, to solve the problem. But the identification rates were not good enough, and Faster R-CNN is very time consuming. As a result we have been trying to find the most suitable algorithm to solve the butterfly species identification challenge. This paper will present some investigations we have undertaken in this field over the past two years, and show the results we have obtained. We propose a new partition and augmentation technique for the extremely unbalanced ecological butterfly database. We found that RetinaNet is, so far, the best deep learning algorithm to tackle butterfly species identification based on butterfly images taken in natural environments. The best result we obtained was 79.7% in terms of mAP (mean average precision). This is the best result compared to the state-of-the-art studies in this field on the same database so far.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. of 62076159, and 12031010, and is also supported by the Fundamental Research Funds for the Central Universities under Grant No. of GK202105003 and 2018TS078, and the Innovation Funds of Graduate Programs at Shaanxi Normal University under Grant No. of 2015CXS028 and 2016CSY009. We also acknowledge those researchers who published the open source codes for us to use in this research.

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, PR China

    Juanying Xie, Yinyuan Lu & Zhaozhong Wu

  2. College of Life Sciences, Shaanxi Normal University, Xi’an, 710119, PR China

    Shengquan Xu

  3. Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK

    Phil W. Grant

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  1. Juanying Xie
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  2. Yinyuan Lu
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  3. Zhaozhong Wu
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  4. Shengquan Xu
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Correspondence to Juanying Xie or Shengquan Xu.

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Xie, J., Lu, Y., Wu, Z. et al. Investigations of butterfly species identification from images in natural environments. Int. J. Mach. Learn. & Cyber. 12, 2431–2442 (2021). https://doi.org/10.1007/s13042-021-01322-8

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  • DOI: https://doi.org/10.1007/s13042-021-01322-8

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