Systematic Harmful Signs Detection for Women’S Safety Using Neural Networks

Nowadays, there was an increase in the extent of disruptive things and offensive things performed toward people, especially women. Due to this, safety has been given preeminent importance and foremost significance [1]. Public locations like purchasing centers, malls, banks, parks, theaters, and numerous others are more and more places are geared with CCTV cameras to make sure safety and security for Women. Constant examination of the CCTV footage by people was an unachievable task for them to locate the danger in it. While it needs a lot of personal and steady interest to find whether the captured things contain suspicious or not suspicious activities in them. This is one of the disadvantages of seeing the danger in the images or videos of things that are captured in them by human beings and their people want to identify the object and which components of the footage include the odd recreation which recognizes the earlier identification of whether every motion is suspicious or not. Therefore, to reduce the time and workforce of humans, we are introducing the use of a ML algorithm recognized as the Darknet 53 algorithm. Its purpose is to find out the symptoms of the weapons in the actual period, which filters out irregularities from regular or common signs. which channels out inconsistencies from normal signs. From that point, the place where we can locate or find an alert for the situation of savagery, showing the dubious things or activities at the precise time of event period on women.