Object detection uses computer vision technique to identify and locate objects in an image or video. This feature can help to improve the security level as it can be deployed to detect a dangerous weapon with object detection methods. Driven by the success of deep learning methods, this study aims to develop and evaluate the use the deep neural network for weapon detection in surveillance videos. The YOLOv3 with Darknet-53 as feature extractor is used for detecting two types of weapons namely pistol and knife. The YOLOv3 Darknet-53 is further improved by optimizing the network backbone. This is achieved by adding a fourth prediction layer and customizing the anchor boxes in order to detect the smaller objects. The proposed model is evaluated with the Sohas weapon detection dataset. The performance of the model is evaluated in terms of precision, recall, mean average precision (mAP) and detection speed in frame per second (FPS).


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    Titel :

    Weapon Detection in Surveillance Videos Using Deep Neural Networks


    Weitere Titelangaben:

    Advances in Engineering res



    Kongress:

    Proceedings of the Multimedia University Engineering ; 2022 ; Cyberjaya and Melaka, Malaysia July 25, 2022 - July 27, 2022



    Erscheinungsdatum :

    23.12.2022


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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