Deep Learning Implementation for Real-Time Egg Counting and Segmentation

• Bruno Raphael Facundo UTFPR http://orcid.org/0009-0001-6373-3434
• Pedro Luiz de Paula Filho UTFPR http://orcid.org/0000-0002-6291-9237
• Juliano Rodrigo Lamb UTFPR https://orcid.org/0000-0003-3417-9312

DOI: https://doi.org/10.5753/latinoware.2024.245186

Abstract

Manual counting of eggs on production conveyors is a process prone to errors and inefficiencies, which can negatively impact productivity and the quality of the final product. Therefore, it is essential to implement solutions that automate this process, increasing the efficiency and accuracy of this task. This paper presents the development of a deep learning model using Python and YOLOv8 for real-time egg counting and segmentation. The model was trained on a dataset of 1,643 images created and annotated with Roboflow, which facilitated the preparation of images for training, testing, and validation. The results demonstrate that the proposed approach achieves high accuracy in egg detection and segmentation, highlighting its potential for implementation in automated systems in the poultry industry.

Keywords: deep learning, egg counting, image segmentation

References

R. V. NUNES, P. C. POZZA, C. G. V. NUNES, E. CAMPESTRINI, R. KÜHL, L. D. ROCHA, and F. G. P. COSTA, “Valores energéticos de subprodutos de origem animal para aves,” Revista Brasileira de Zootecnia, vol. 34, no. 4, p. 1217–1224, Jul 2005. [Online]. Available: DOI: 10.1590/S1516-35982005000400017

I. KANJANASURAT, W. KRUNGSEANMUANG, V. CHAOWALITTAWIN, and B. PURAHONG, “Egg-counting system using image processing and a website for monitoring,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2021, pp. 101–104. [Online]. Available: DOI: 10.1109/ICEAST52143.2021.9426295

B. ROMÃO, “Redes neurais convolucionais para a detecção de objetos,” Master’s thesis, Pontifícia Universidade Católica de Campinas, Apr. 2023, dissertação de mestrado. [Online]. Available: [link]

T. M. LEITE, “Deep learning em dois estágios para detecção e classificação de doenças em folhas de plantas com aplicação em dispositivos móveis,” Dissertação de Mestrado, USP, Instituto de Ciências Matemáticas e de Computação, São Carlos, 2021. [Online]. Available: DOI: 10.11606/D.55.2021.tde-02022022-121135

J. ALIKHANOV, S. M. PENCHEV, T. D. GEORGIEVA, A. MOLDAZHANOV, A. KULMAKHAMBETOVA, Z. SHYNYBAY, E. STEFANOV, and P. I. DASKALOV, “Design and performance of an automatic egg sorting system based on computer vision,” TEM Journal, vol. 8, no. 4, pp. 1319–1325, 2019. [Online]. Available: DOI: 10.18421/TEM84-31

A. VINOD, D. MOHANTY, A. JOHN, and B. DEPURU, “Application of artificial intelligence in poultry farming - advancing efficiency in poultry farming by automating the egg counting using computer vision system,” 08 2023. [Online]. Available: DOI: 10.21203/rs.3.rs-3266412/v1

D. WU, D. CUI, M. ZHOU, Y. WANG, J. PAN, and Y. YING, “Using yolov5-dse for egg counting in conventional scale layer farms,” IEEE Transactions on Industrial Informatics, pp. 1–10, 2024. [Online]. Available: DOI: 10.1109/TII.2024.3452270

R. C. GONZALEZ and R. E. WOODS, Processamento Digital de Imagens, 3rd ed. Editora Blucher, 2009. [Online]. Available: [link] ots=zinFRIcVui&dq=Processamento%20Digital%20de%20Imagens%20gonzalez&lr&hl=pt-PT&pg=PA1#v=onepage&q&f=false

M. F. S. SALDANHA and C. C. FREITAS, “Segmentação de imagens digitais: Uma revisão” in Anais do 9º Workshop dos Cursos de Computação Aplicada do INPE. São José dos Campos: INPE, 2009. [Online]. Available: [link]

J. TERVEN, D. M. CORDOVA-ESPARZA, and J. A. ROMERO-GONZÁLEZ, “A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas,” Mach. Learn. Knowl. Extr., vol. 5, no. 4, pp. 1680–1716, 2023. [Online]. Available: DOI: 10.3390/make5040083

E. FIGUEIREDO and E. SILVA, “Combate ao covid19: Detecção em tempo real de indivíduos sem máscara em ambiente escolar por meio de deep learning,” in Anais do XV Brazilian e-Science Workshop. Porto Alegre, RS, Brasil: SBC, 2021, pp. 113–120. [Online]. Available: [link]

I. GOODFELLOW, Y. BENGIO, and A. COURVILLE, Deep Learning. MIT Press, 2016. [Online]. Available: [link]

I. LOSHCHILOV and F. HUTTER, “Decoupled weight decay regularization,” 2019. [Online]. Available: [link]

D. P. KINGMA and J. BA, “Adam: A method for stochastic optimization,” 2017. [Online]. Available: [link]

S. WANG, X. CAO, M. WU, C. YI, Z. ZHANG, H. FEI, H. ZHENG, H. JIANG, Y. JIANG, X. ZHAO, X. ZHAO, and P. YANG, “Detection of pine wilt disease using drone remote sensing imagery and improved yolov8 algorithm: A case study in weihai, china,” Forests, vol. 14, no. 10, p. 2052, 2023. [Online]. Available: DOI: 10.3390/f14102052

S. MINAEE, Y. BOYKOV, F. PORIKLI, A. PLAZA, N. KEHTARNAVAZ, and D. TERZOPOULOS, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523–3542, 2022. [Online]. Available: DOI: 10.1109/TPAMI.2021.3059968

H. HUANG, G. LAN, J. WEI, Z. ZHONG, Z. XU, D. LI, and F. ZOU, “Tli-yolov5: A lightweight object detection framework for transmission line inspection by unmanned aerial vehicle,” Electronics, vol. 12, no. 15, p. 3340, 2023. [Online]. Available: DOI: 10.3390/electronics12153340