gun object detection
This paper presents a deep learning model for fast and accurate radar detection and pixel-level localization of large concealed metallic weapons on pedestrians walking along a sidewalk. The considered radar is stationary, with a multi-beam antenna operating at 30 GHz with 6 GHz bandwidth. A large modeled data set has been generated by running 2155 2D-FDFD simulations of torso cross sections of persons walking toward the radar in various scenarios.
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I have prepared the dataset for this tutorial which you can download from here. This dataset contains around 3000 handgun images with their bounding box labels distributed in 2 folders: images and labels
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