Weapon detection from camera footages using YOLO and SSD models / Meriem Zaoui

Public ISBD Titre : Weapon detection from camera footages using YOLO and SSD models Type de document : texte manuscrit Auteurs : Meriem Zaoui, Auteur ; Asma Guerroudj, Auteur ; Leila Benarous, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2022 Importance : 43 p. Format : 30 cm. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Networks, systems, and distributed applications (Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Weapon detection  Convolutional Neural Networks (CNN)  YOLOV3  YOLOV4  SSD mobile net  CCTV Résumé : detection using a convolutional neural network (CNN) based SSD mobile network YOLOV3 and the YOLOV4 algorithm. The purpose behind using three models was to compare between their accuracy and investigate their potential use and suitability in real-time environment. The results for the three models were good. However, in term of accuracy, YOLOV4 showed more promising results followed by the SSD model then theYOLOV3. Even though, the accuracy is not the only criterion to consider in real-world applications requiring short latency and high speed. Therefore, taking this tradeoff between accuracy and speed the YOLOV4 model seems to be the most suitable. note de thèses : Mémoire de master en informatique Weapon detection from camera footages using YOLO and SSD models [texte manuscrit] / Meriem Zaoui, Auteur ; Asma Guerroudj, Auteur ; Leila Benarous, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2022 . - 43 p. ; 30 cm. + 1 disque optique numérique (CD-ROM). Option : Networks, systems, and distributed applications (Réseaux,systèmes et applications réparties) Langues : Anglais Mots-clés : Weapon detection  Convolutional Neural Networks (CNN)  YOLOV3  YOLOV4  SSD mobile net  CCTV Résumé : detection using a convolutional neural network (CNN) based SSD mobile network YOLOV3 and the YOLOV4 algorithm. The purpose behind using three models was to compare between their accuracy and investigate their potential use and suitability in real-time environment. The results for the three models were good. However, in term of accuracy, YOLOV4 showed more promising results followed by the SSD model then theYOLOV3. Even though, the accuracy is not the only criterion to consider in real-world applications requiring short latency and high speed. Therefore, taking this tradeoff between accuracy and speed the YOLOV4 model seems to be the most suitable. note de thèses : Mémoire de master en informatique

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