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

Archive ouverte : Communication dans un congrès

He, Ruiwen | Benhabiles, Halim | Windal, Feryal | Even, Gael | Audebert, Christophe | Collard, Dominique | Taleb-Ahmed, Abdelmalik

Edité par HAL CCSD ; IEEE

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.

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