weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, …

Feb 12, 2021 · This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as the reference class and relevant confusion objects inclusion concept is introduced to reduce false ...

This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually.

This work, focuses on automatic weapon detection in CCTV footage by making use of deep learning algorithms. In our work we are going to use YOLOv8 model to detect weapons as it is better in terms of accuracy and speed when compared to other models.

This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of

By leveraging the YOLOv4 model’s ability to detect objects in real-time with a single pass through the network, our proposed system achieves effi cient and reliable weapon detection in diverse scenarios.

This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SS D and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually.

This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SS D and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelledmanually.

The methodology for weapon detection using deep learning is illustrated in Figure 1. The process begins with extracting frames from the input video. Each frame is then processed using a frame differencing algorithm, which helps identify changes between consecutive frames.

Jul 1, 2020 · This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SSD and Faster RCNN algorithms. Proposed implementation uses two types of datasets.