Invertebrates detection with YOLOv5: Towards study of soil organisms using deep learning

Pruvost Emma, Tulet Hadrien, Delort Etienne, Shokouh Ghulam Sakhi, Montesinos Philippe, Magnier Baptiste, Jourdan Christophe, Belaud Emma, Hedde Mickaël. 2022. Invertebrates detection with YOLOv5: Towards study of soil organisms using deep learning. In : 2022 10th European Workshop on Visual Information Processing (EUVIP): Proceedings. IEEE. New-York : IEEE, 232-237. ISBN 978-1-6654-6623-3 European Workshop on Visual Information Processing (EUVIP). 10, Lisbonne, Portugal, 11 Septembre 2022/14 Septembre 2022.

https://doi.org/10.1109/EUVIP53989.2022.9922675

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Résumé : The investigation of the complicated underground life via automatic technique is in high demand in recent days. Using Convolutional Neural Network (CNN) to detect soil invertebrates is an interesting approach, although most studies on the topic have focused on other solutions. The creation of state-of-the-art technique through this work will be a significant step in soil ecology, bio-science and agriculture in effectively exploring the different types of invertebrates, their behaviors and interactions. In this paper, generating and annotating images containing seven classes of invertebrates is firstly presented. Then various automatic detections of the invertebrates using YOLOv5 algorithm on these images are performed and evaluated.

Agences de financement hors UE : Ministère Français de l'Europe et des Affaires Etrangères

Projets sur financement : (FRA) ClimaTe Resilient lAndscapes for wIldLife conservation

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