Smart Vision System for Sheep Face Detection using Deep Neural Networks / Lobna Chellali

Public ISBD Titre : Smart Vision System for Sheep Face Detection using Deep Neural Networks Type de document : document multimédia Auteurs : Lobna Chellali, Auteur ; Nermine Bouzaher, Auteur ; Mohamed Bouzidi, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'électronique Année de publication : 2024 Importance : 60p. Accompagnement : cd rom Note générale : Degree in Network And Telecommunications Langues : Anglais Mots-clés : Deep learning  SSD MobileNet  CenterNet Hourglass  AI Résumé : This thesis investigates the application of deep learning techniques to sheep face detection, focusing on two prominent object detection models: SSD MobileNet 320x320 and CenterNet Hourglass 512x512. Our study aims to enhance livestock management by providing an efficient method for identifying individual sheep within a flock. By leverag ing advanced object detection algorithms, we demonstrate significant improvements in the accuracy and efficiency of sheep identification. The results indicate that while CenterNet Hourglass 512x512 achieves higher accuracy, SSD MobileNet 320x320 offers faster pro cessing times. This research contributes to the field of agricultural AI, offering practical solutions for enhanced livestock monitoring and management. note de thèses : memoire de master en Electronique Smart Vision System for Sheep Face Detection using Deep Neural Networks [document multimédia] / Lobna Chellali, Auteur ; Nermine Bouzaher, Auteur ; Mohamed Bouzidi, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'électronique, 2024 . - 60p. + cd rom. Degree in Network And Telecommunications Langues : Anglais Mots-clés : Deep learning  SSD MobileNet  CenterNet Hourglass  AI Résumé : This thesis investigates the application of deep learning techniques to sheep face detection, focusing on two prominent object detection models: SSD MobileNet 320x320 and CenterNet Hourglass 512x512. Our study aims to enhance livestock management by providing an efficient method for identifying individual sheep within a flock. By leverag ing advanced object detection algorithms, we demonstrate significant improvements in the accuracy and efficiency of sheep identification. The results indicate that while CenterNet Hourglass 512x512 achieves higher accuracy, SSD MobileNet 320x320 offers faster pro cessing times. This research contributes to the field of agricultural AI, offering practical solutions for enhanced livestock monitoring and management. note de thèses : memoire de master en Electronique

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