Counting instances of objects in color images using U-net network on example of honey bees

• Autorzy: Westwańska Weronika W. , Respondek Jerzy S.
• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

This article presents a novel approach to segmentation and counting of objects in color digital images. The objects belong to a certain class, which in this case are honey bees. The authors briefly present existing approaches which use Convolutional Neural Networks to solve the problem of image segmentation and object recognition. The focus however is on application of U-Net convolutional neural network in an environment where knowledge about the object of interest is only limited to its rough, single pixel location. The authors provide full access to the details of the code used to implement the algorithms, as well as the data sets used and results obtained. The results show an encouraging low level of counting error at 14.27% for the best experiment.

Słowa kluczowe

EN

convolutional neural nets; image colour analysis; image segmentation; object recognition

PL

splotowe sieci neuronowe; analiza koloru obrazu; segmentacja obrazu; rozpoznawanie obiektów

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 87--90
• Opis fizyczny: Bibliogr. 15 poz., tab., wykr.

Twórcy

autor

Westwańska Weronika W.

• Zespół Szkół Stowarzyszenia Rodzin Katolickich Archidiecezji Katowickiej im. Kardynała Prymasa Augusta Hlonda, ul. Kościuszki 11, 41-500 Chorzów, Poland

autor

Respondek Jerzy S.

• Silesian University of Technology, Institute of Informatics, AEI Faculty, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

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• 7. https://www.kaggle.com/jonathanbyrne/to-bee-or-not-to-bee, accessed on the 1st of February 2019.
• 8. M. Kelcey, “Counting bees on a rasp pi with a conv net”, 2018. http://matpalm.com/blog/counting_bees
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• 13. J.P. Vigueras-Guillén,B. Sari, S.F. Goes, H.G. Lemij, J. van Rooij, K.A. Vermeer, L.J. van Vliet, "Fully convolutional architecture vs sliding-window CNN for corneal endothelium cell segmentation", BMC Biomedical Engineering, vol. 1, 2019. https://doi.org/10.1186/s42490-019-0003-2
• 14. S. Baek, Y. He, B.G. Allen, J.M. Buatti, B.J. Smith, K. A. Plichta, et al. “What does AI see? Deep segmentation networks discover biomarkers for lung cancer survival”, 2019. https://arxiv.org/abs/1903.11593
• 15. https://github.com/WeronikaWestwanska/ToBeOrNotToBee, accessed on the 8th of May 2019.

Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).