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Dragonflies segmentation with U-Net based on cascaded ResNeXt cells

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Abstract

In cooperation with biologists, we discuss the problem of animal species protection with the usage of modern technologies, namely mobile phones. In our work, we consider the problem of dragonfly image classification, where the aim is given to a preprocessing—segmentation of a dragonfly body from a background. To solve the task, we improve U-Net architecture by ResNeXt cells firstly. Further, we focus on the reasonability of features in neural networks with cardinality dimension and propose the cascaded way of re-using the features among blocks in particular cardinal dimensions. The reuse of the already trained features leads to composing more robust features and more efficient usage of neural network parameters. We test our cascaded cells together with three various U-Net versions for four different settings of hyperparameters with the conclusion that the system of cascaded features leads to higher accuracy than the other versions with the same number of parameters. Also, the cascaded cells are more robust to overfitting the dataset. The obtained results are confirmed on two additional public datasets.

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Notes

  1. http://www.kaggle.com/c/carvana-image-masking-challenge.

  2. http://celltrackingchallenge.net/.

  3. colab.research.google.com/drive/1575oeJWafK9biTGOYmzXMsqdQVoyi50d.

  4. http://www.kaggle.com/c/carvana-image-masking-challenge.

  5. http://www.kaggle.com/c/tgs-salt-identification-challenge.

References

  1. Sandra D, Joseph Fargione F (2006) Stuart Chapin III, and David Tilman. Biodiversity loss threatens human well-being. PLoS Biol 4(8):277

    Article  Google Scholar 

  2. Clausnitzer V, Kalkman VJ, Ram M, Collen B, Baillie JEM, Bedjanič M, Darwall WRT, Klaas-Douwe BDijkstra, Rory Dow, John Hawking, et al (2009) Odonata enter the biodiversity crisis debate: the first global assessment of an insect group. Biol Conserv 142(8):1864–1869

    Article  Google Scholar 

  3. ThomasE K, JasonT B (2014) Adult odonata conservatism as an indicator of freshwater wetland condition. Ecol Indicat 38:31–39

    Article  Google Scholar 

  4. Seidu I, Nsor CA, Danquah E, Lancaster L (2018) Odonata assemblages along an anthropogenic disturbance gradient in ghana’s eastern region. Odonatologica

  5. Martín R, Maynou X (2016) Dragonflies (insecta: Odonata) as indicators of habitat quality in mediterranean streams and rivers in the province of barcelona (catalonia, iberian peninsula). Int J Odonatol 19(3):107–124

    Article  Google Scholar 

  6. de Paiva SD, De Marco P, Resende DC (2010) Adult odonate abundance and community assemblage measures as indicators of stream ecological integrity: a case study. Ecol Indicat 10(3):744–752

    Article  Google Scholar 

  7. Kalkman VJ, Clausnitzer V, Dijkstra K-DB, Orr AG, Paulson DR, van Tol J (2007) Global diversity of dragonflies (odonata) in freshwater. In: Freshwater animal diversity assessment. Springer, pp 351–363

  8. Jeno LM, Grytnes J-A, Vandvik V (2017) The effect of a mobile-application tool on biology students’ motivation and achievement in species identification: a self-determination theory perspective. Comput Educ 107:1–12

    Article  Google Scholar 

  9. Ožana S, Burda M, Hykel M, Malina M, Prášek M, Bárta D, Dolnỳ A (2019) Dragonfly hunter cz: mobile application for biological species recognition in citizen science. PloS One 14(1):e0210370

    Article  Google Scholar 

  10. Yeager WC (1932) Some dragonflies of northwest Iowa. In: Proceedings of the Iowa Academy of Science, vol 39, pp 261–263

  11. Long J, Shelhamer Evan D (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  12. Zhao H, Qi X, Shen X, Shi J, Jia J (2018) Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European conference on computer vision (ECCV), pp 405–420

  13. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  14. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890

  15. Chaurasia A, Culurciello E (2017) Linknet: exploiting encoder representations for efficient semantic segmentation. In: IEEE visual communications and image processing (VCIP). IEEE, pp 1–4

  16. Ross G, Jeff D, Trevor D, Jitendra M (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  17. Bennett KP, Campbell C (2000) Support vector machines: hype or hallelujah? ACM SIGKDD Explor Newsl 2(2):1–13

    Article  Google Scholar 

  18. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448

  19. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  20. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In Proceedings of the IEEE international conference on computer vision, pp 2961–2969

  21. Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767

  22. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

  23. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  24. Ning F, Delhomme D, LeCun Y, Piano F, Bottou L, Barbano PE (2005) Toward automatic phenotyping of developing embryos from videos. IEEE Trans Image Process 14:1360–1371

    Article  Google Scholar 

  25. Hinton GE, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: International conference on artificial neural networks. Springer, pp 44–51

  26. Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: International conference on artificial neural networks. Springer, pp 92–101

  27. Hanin Boris (2018) Which neural net architectures give rise to exploding and vanishing gradients? In: Advances in neural information processing systems, pp 582–591

  28. Vesal S, Ravikumar N, Maier A (2019) A 2d dilated residual u-net for multi-organ segmentation in thoracic ct. arXiv preprint arXiv:1905.07710

  29. Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geosci Rem Sens Lett 15(5):749–753

    Article  Google Scholar 

  30. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  31. Xie S, Girshick R, Dollár P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1492–1500

  32. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  33. Wells WM (1986) Efficient synthesis of gaussian filters by cascaded uniform filters. IEEE Trans Pattern Anal Mach Intell 2:234–239

    Article  Google Scholar 

  34. Canny J (1987) A computational approach to edge detection. In: Readings in computer vision. Elsevier, pp 184–203

  35. Sato Y, Tamura S (1988) Design methods for cascaded gaussian filters. Syst Comput Jpn 19(12):24–34

    Article  Google Scholar 

  36. Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12

    Article  Google Scholar 

  37. Zeiler Matthew D (2012) Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701

  38. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814

  39. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  40. Wan L, Zeiler M, Zhang S, Cun YL, Fergus R (2013) Regularization of neural networks using dropconnect. In: International conference on machine learning, pp 1058–1066

  41. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  42. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  43. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, pp 249–256

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Acknowledgements

This research was supported by the project “LQ1602 IT4Innovations excellence in science” and by Student Grant Competition of University of Ostrava (no. SGS06/ UVAFM/2019). For more supplementary materials and overview of our laboratory work, see http://www.graphicwg.irafm.osu.cz/storage/pr/links.html.

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Authors and Affiliations

  1. Centre of Excellence IT4Innovations Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, 30. dubna 22, Ostrava, Czech Republic

    Petr Hurtik

  2. Department of Biology and Ecology, Faculty of Science, University of Ostrava, Chittussiho 10, Ostrava, Czech Republic

    Stanislav Ozana

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  1. Petr Hurtik
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Correspondence to Petr Hurtik.

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Hurtik, P., Ozana, S. Dragonflies segmentation with U-Net based on cascaded ResNeXt cells. Neural Comput & Applic 33, 4567–4578 (2021). https://doi.org/10.1007/s00521-020-05274-y

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