Classification methods of butterfly images based on U-net and STL-MSDNet
Tóm tắt
Aiming at the lack of coarse-grained features in butterfly image classification and recognition research, low recognition accuracy, and limited spatial invariance to input data, this paper proposes a butterfly image classification method based on U-Net and STL-MSDNet. Firstly, in order to reduce the influence of complex backgrounds on butterfly image recognition, the U-Net model is used to segment the butterfly ecological image. Then, an STL-MSDNet model is proposed to classify butterfly images. In STL-MSDNet, Spatial Transformer Network (STN) is added to reverse the spatial transformation of butterfly images to eliminate the deformation of image butterflies and make the recognition of the classification network simpler and more efficient. Then, the Laplace pyramid (LP) was introduced to replace gaussian down-sampling in MSDNet, and the butterfly images were decomposed into different spatial frequency bands to obtain butterfly feature maps of three scales, and then they were fused to improve the feature extraction capability of the network. The experimental results show that the butterfly image semantic segmentation algorithm based on U-Net has a good effect and is suitable for the field of image segmentation in complex backgrounds. Compared with MSDNet, DenseNet and traditional classification algorithms, the butterfly image classification model based on STL-MSDNet proposed in this paper has a better effect, better robustness, and a higher recognition rate. The method proposed in this paper solved the problem of low accuracy of classification of butterfly images in complex backgrounds by existing methods, and obtains a classification accuracy of 93.8%, indicating that it has good results in the fine classification of butterflies and can be applied to butterfly identification and realize the application of butterfly ecological research.
Tài liệu tham khảo
Chen Y, Feng F, Yuan ZM (2011) Automatic identification of butterfly species with an improved support vector classification. Acta Entomol Sin 54:609–614. https://doi.org/10.16380/j.kcxb.2011.05.017
Dai J, Li Y, He K et al (2016) R-fcn: Object detection via region-based fully convolutional networks Advances in neural information processing systems, 29. https://arxiv.org/abs/1605.06409. Accessed 3 Dec 2020
Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Scie Rev 40:100379. https://doi.org/10.1016/j.cosrev.2021.100379
Fan L (2015) The research on automatic identification of butterfly species based on the digital image. Master Dissertation. Beijing Forestry University, China https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2015&filename=1015319461.nh. Accessed 13 Nov 2020
Girshick R (2015) Fast r-cnn. Proceedings of the IEEE international conference on computer vision, 1440-1448. https://doi.org/10.1109/ICCV.2015.169.
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res 2010(9):249–256 https://www.researchgate.net/publication/215616968
He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. IEEE international conference on computer vision. IEEE, 1026-1034, https://doi.org/10.1109/ICCV.2015.123.
He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In proceedings of the IEEE conference on computer vision and pattern recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90.
Hernández-Serna A, Jiménez-Segura LF (2014) Automatic identification of species with neural networks. PeerJ 2:e563 https://peerj.com/articles/563
Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. IEEE conference on computer vision and pattern recognition, 2261-2269. https://doi.org/10.1109/CVPR.2017.243.
Jaderberg M, Simonyan K, Zisserman A (2015) Spatial transformer networks. Adv Neural Inf Proces Syst, 28. https://ui.adsabs.harvard.edu/abs/2015arXiv150602025J. Accessed 27 Oct 2020
Jégou S, Drozdzal M, Vazquez D et al (2017) The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In proceedings of the IEEE conference on computer vision and pattern recognition workshops, 11-19. https://arxiv.org/abs/1611.09326. Accessed 11 Nov 2020
Kang SH, Cho JH, Lee SH (2014) Identification of butterflybased oil their shapes when viewed fromdifferent angles using an artificial neural network. J Asia Pac Entomol 7:143–149. https://doi.org/10.1016/j.aspen.2013.12.004
Kaya Y, Kayci L (2013) Application of artificial neural network for automatic detection of butterfly species using color and texture features. Vis Comput 30:71–79. https://doi.org/10.1007/s00371-013-0782-8
Kaya Y, Kayci L, Tekin R (2013) A computer vision system for the automatic identification of butterfly species via gabor-filter-based texture features and extreme learning machine: GF+ ELM. TEM J 2:13–20 https://www.researchgate.net/publication/284466369
Keserwani P, Roy PP (2021) Text region conditional generative adversarial network for text concealment in the wild. IEEE Trans Circ Syst Vid Technol 32:3152–3163. https://doi.org/10.1109/TCSVT.2021.3103922
Lin G, Shen C, Van Den Hengel A et al (2017) Exploring context with deep structured models for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 40:1352–1366. https://doi.org/10.1109/TPAMI.2017.2708714
Lin G, Milan A, Shen C et al (2017) RefineNet: multi-path refinement networks for high-resolution segmentation proceedings of the IEEE conference on computer vision and pattern recognition, 1925-1934. https://doi.org/10.1109/CVPR.2017.549
Liu F (2007) The application of Wings’Color characters in butterfly species automatic identification. Dissertation, China Agricultural University, Beijing, China. https://doi.org/10.7666/d.y1107954
Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640–651. https://doi.org/10.1109/TPAMI.2016.2572683
Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In proceedings of tlie IEEE international conference on computer vision, 1520-1528. https://arxiv.org/abs/1505.04366
Pan PL, Shen ZR, Gao LW et al (2008) Development of the technology for auto-extracting venation of insects. Entomotaxonomia 30:72–80. https://doi.org/10.3969/j.issn.1000-7482.2008.01.018
Pan PL, Yang HZ, Shen ZR et al (2008) Research on applying vein feature for mathematical morphology in classification and identification of butterflies (lepidoptera: Rhopalocera). Entomotaxonomia 30:151–160. https://doi.org/10.3969/j.issn.1000-7482.2008.02.013
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 234–241. https://doi.org/10.1007/978-3-319-24574-4_28.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://arxiv.org/abs/1409.1556.
Sutskever I, Martens J, Dahl G et al (2013) On the importance of initialization and momentum in deep learning. International conference on international conference on machine learning. PMLR, 1139-1147. https://doi.org/10.1007/s00287-015-0911-z.
Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition, 1–9. https://doi.org/10.1109/CVPR.2015.7298594.
Xiao S, Ting P, Fu-Ji R (2016) Facial expression recognition using ROI-KNN deep convolutional neural networks. Acta Autom Sin 42:883–891. https://doi.org/10.16383/j.aas.2016.c150638
Xie J, Hou Q, Shi Y et al (2018) The automatic identification of butterfly species. J Comput Res Dev 55:16–1618. https://doi.org/10.7544/issn1000-1239.2018.20180181
Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122. https://arxiv.org/abs/1511.07122
Yu LH, Liu NZ, Zhou WG, Dong S, Fan Y, Abbas K (2021) Weber’s law based multi-level convolution correlation features for image retrieval. Multimed Tools Appl 80:19157–19177. https://doi.org/10.1007/s11042-020-10355-0
Zeiler M D, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557. https://arxiv.org/abs/1301.3557
Zhang JW (2006) Automatic identification of butterflies based on computer vision technology. Dissertation, China Agricultural University, Beijing, China. https://doi.org/10.7666/d.y940039
Zhao H, Shi J, Qi X et al (2017) Pyramid scene parsing network. IEEE Conf.On computer vision and pattern recognition (CVPR), 2881-2890, https://doi.org/10.1109/CVPR.2017.660.
Zhou AM, Ma PP, Xi TY et al (2017) Automatic identification of butterfly specimen images at the family level based on deep learning method. Acta Entomol Sin 60:1339–1348. https://doi.org/10.16380/j.kcxb.2017.11.012