  Automatic detection of weapons is significant for improving security and well
being of individuals, nonetheless, it is a difficult task due to large variety
of size, shape and appearance of weapons. View point variations and occlusion
also are reasons which makes this task more difficult. Further, the current
object detection algorithms process rectangular areas, however a slender and
long rifle may really cover just a little portion of area and the rest may
contain unessential details. To overcome these problem, we propose a CNN
architecture for Orientation Aware Weapons Detection, which provides oriented
bounding box with improved weapons detection performance. The proposed model
provides orientation not only using angle as classification problem by dividing
angle into eight classes but also angle as regression problem. For training our
model for weapon detection a new dataset comprising of total 6400 weapons
images is gathered from the web and then manually annotated with position
oriented bounding boxes. Our dataset provides not only oriented bounding box as
ground truth but also horizontal bounding box. We also provide our dataset in
multiple formats of modern object detectors for further research in this area.
The proposed model is evaluated on this dataset, and the comparative analysis
with off-the shelf object detectors yields superior performance of proposed
model, measured with standard evaluation strategies. The dataset and the model
implementation are made publicly available at this link:
https://bit.ly/2TyZICF.
