Publication detail

Computer Vision Approaches for Automated Bee Counting Application

BILÍK, Š. JANÁKOVÁ, I. LIGOCKI, A. FICEK, D. HORÁK, K.

Original Title

Computer Vision Approaches for Automated Bee Counting Application

Type

conference paper

Language

English

Original Abstract

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.

Keywords

Signal Processing; Biomedical systems; bee counting; bee traffic monitoring; computer vision; deep classification; object detection

Authors

BILÍK, Š.; JANÁKOVÁ, I.; LIGOCKI, A.; FICEK, D.; HORÁK, K.

Released

21. 6. 2024

Publisher

Brno University of Technology

Location

Brno, CZ

ISBN

2405-8963

Periodical

IFAC-PapersOnLine (ELSEVIER)

Year of study

58

Number

9

State

Kingdom of the Netherlands

Pages from

43

Pages to

48

Pages count

6

URL

https://www.sciencedirect.com/science/article/pii/S2405896324004580?via%3Dihub

BibTex

@inproceedings{BUT189322,
  author="Šimon {Bilík} and Ilona {Janáková} and Adam {Ligocki} and Dominik {Ficek} and Karel {Horák}",
  title="Computer Vision Approaches for Automated Bee Counting Application",
  booktitle="18th IFAC Conference on Programmable Devices and Embedded Systems PDES 2024",
  year="2024",
  journal="IFAC-PapersOnLine (ELSEVIER)",
  volume="58",
  number="9",
  pages="43--48",
  publisher="Brno University of Technology",
  address="Brno, CZ",
  doi="10.1016/j.ifacol.2024.07.369",
  issn="2405-8963",
  url="https://www.sciencedirect.com/science/article/pii/S2405896324004580?via%3Dihub"
}