Weapon detection from camera footages using YOLO and SSD models

dc.contributor.authorZaoui, Meriem
dc.contributor.authorGuerroudj, Asma
dc.contributor.authorBenarous, Leila
dc.date.accessioned2023-01-10T14:50:37Z
dc.date.available2023-01-10T14:50:37Z
dc.date.issued2022
dc.description.abstractDetection 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.
dc.identifier.urihttps://dspace.lagh-univ.dz/handle/123456789/1069
dc.language.isoen
dc.publisherUniversité Amar Telidji - Laghouat - Département d'informatique
dc.titleWeapon detection from camera footages using YOLO and SSD models
dc.typeThesis

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