Published Paper Details:

In today's modern world, safety and security are big concerns.Daily, banks and shops are robbed, and the ratio of robberies is increasing eventually.So, there is a need for such a safety system that minimizes the chances of robbery and helps keep the peace and safety maintained. Mostly CCTV (Closed-circuit Television) the surveillance system is used, which is quite inefficient due to the dependency on the human factor. Adding ML (Machine Learning) i.e., Weapon Detection in the system will not only be efficient in discernment but will also identify the potential threat in much less time. This project uses the latest object detection technique YOLO (You Only Look Once) to identify handguns and rifles, with training on the data set of images. Whenever the weapon is detected, the system will alert .This project will be implemented using python and Machine Learning algorithm YOLO to detect weapons on the input video or image.In conclusion, weapon detection system can provide the early detection of potentially violent situations that is of paramount importance for citizens security. In this research work, we aim to develop a smart surveillance security system detecting weapons specifically guns. For this purpose, we have applied few compute vision methods and machine learning for identification of a weapon from captured image. Recent work in the field of machine learning and deep learning particularly convolutional neural networks has shown considerable progress in the areas of object detection and recognition, exclusively in images.As the first step for any video surveillance application, object detection and classification are essential for further object tracking tasks.