Weapon Detection from Surveillance Images using Deep Learning

Security is the biggest concern in today’s world which needs to be addressed to save people from critical threats. We need to detect these threats at the earliest to protect people and take required actions. Security cameras are used almost everywhere now ranging from our home to shopping malls to banks. Currently, not many surveillance cameras have an automatic weapon detection system but with the advancement in technologies, it can be easily equipped. This will help the people in charge concerned to take the appropriate actions and thus prevent crimes. Deep learning techniques are used widely to detect objects as the traditional methods of object detection have their own limitations in certain situations. One such algorithm – Mask RCNN is implemented in this work to detect guns from surveillance video images. Gaussian deblur technique is used to enhance the features of handgun for efficient detection especially in blurred images. The experiment results show that the performance of the model increased with preprocessing.