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A Cervix Detection Driven Deep Learning Approach for Cow Heat Analysis from Endoscopic Images

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  • معلومة اضافية
    • Contributors:
      Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN); Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA); Université catholique de Lille (UCL)-Université catholique de Lille (UCL); Bio-Micro-Electro-Mechanical Systems - IEMN (BIOMEMS - IEMN); Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA); JUNIA (JUNIA); Université catholique de Lille (UCL); Gènes Diffusion Douai; Laboratory for Integrated Micro Mechatronics Systems (LIMMS); The University of Tokyo (UTokyo)-Centre National de la Recherche Scientifique (CNRS); COMmunications NUMériques - IEMN (COMNUM - IEMN); INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN); Université Polytechnique Hauts-de-France (UPHF); This project has been funded by the FEDER European program, JUNIA French Engineering school and G`enes Diffusion French company.The authors gratefully acknowledge Claude Grenier, CEO Genes Diffusion, Pierrick Drevillon CEO CECNA, Olivier Darasse, CEO Elexinn for the availability of data and the labeling of the videos.
    • بيانات النشر:
      HAL CCSD
      IEEE
    • الموضوع:
      2022
    • Collection:
      Université Polytechnique Hauts-de-France: HAL
    • الموضوع:
    • نبذة مختصرة :
      International audience ; In this article, we propose a new approach for the cow heat detection from endoscopic images. Our approach permits to identify on the fly the cow heat state through two successive stages, namely cervix detection then heat classification. For this purpose, images are analyzed by a Transformer based detection model to localize the cervix, in which case they are analyzed by a CNN-based heat classification model. The proposed approach permits to assist the farmer during the insemination operation by localizing the cervix in an accurate way. Moreover, the confidence level of the final decision of the classification model is increased by focusing its analysis only on cervix images. The effectiveness of our method is demonstrated on our generated dataset and the obtained performance outperform the state of the art.
    • Relation:
      hal-03839222; https://hal.science/hal-03839222; https://hal.science/hal-03839222/document; https://hal.science/hal-03839222/file/halim_icip_vesionpreprint_A%20Cervix%20Detection%20Driven%20Deep%20Learning%20Approach%20for%20Cow%20Heat%20Analysis%20from%20Endoscopic%20Images.pdf
    • الرقم المعرف:
      10.1109/ICIP46576.2022.9897442
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.680691CE