Multiplatform Surveillance System for Weapon Detection using YOLOv5

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) surveillance system is used, which is quite inefficient due to the dependency on the human factor. Adding AI (Artificial Intelligence) i.e., Object Detection in the system will not only be efficient in discernment but will also identify the potential threat in much less time. The project uses the latest object detection technique YOLO (You Only Look Once) to identify handguns and rifles, with training on the data set of 7801 images, through a camera, implemented on Raspberry Pi/Jetson Nano. The GUI (Graphical User Interface) can be operated from a computer system (Web Portal, created using HTML-CSS) as well as on a mobile android application (created using android studio) therefore, giving the project the name of ‘MULTIPLATFORM’. Whenever the weapon is detected, the system sends the screenshot, along with an alert via NODEMCU (ESP8266) on the web portal of manager/user's system, which gives two options; either discard the request with the green button or affirm the threat with the red button. Once the manager/user confirms the situation as dangerous, the information is sent to nearby concerned authorities i.e., police stations via a message/call using the GSM module. If the alert is discarded, everything resets to its original state. Also, if the manager doesn't respond to the system within 15 seconds, the concerned authorities will be notified accordingly. In the end, a fake scenario of robbery was depicted in which the developed system detected the weapon hence successfully demonstrating the working.