Deep Learning based Gender Responsive Smart Device to Combat Domestic Violence
Deepa V. Jose · SPAST Abstracts · 2021
Around the clock, women face harassment and violence. Most of the time it involves harmful weapons. As this is increasing in an enormous rate, police officers are challenged to bring the situation under control time after time. In some rural areas, there are no laws regarding weapon prohibition, which questions the security of the citizens residing there. Hence, this paper aims to provide a unique solution that can prevent any mishaps as well as predict the crime well in advance. The basic idea is to detect harmful weapons such as knives and pistols as well as any suspicious activities in the surroundings. Deep Learning and transfer learning have proven to produce significant results in the field of image processing. The agenda of this paper is to develop a thoroughly automated computer-based system to detect any harmful weapons mainly pistols and knives. This is done [3] by using YOLO (You Only Look Once), a deep learning algorithm, for successful real time detection of weapons. Although there are other algorithms for object detection namely CNN (Convolutional Neural Networks), whose variants include R-CNN (Region based convolutional neural networks) and F-CNN (Faster convolutional neural networks) and SSD (Single Shot Multi-box detector, YOLO is highly preferred because of its speed and accuracy, and also its ability to pass only one image once through the neural network. The dataset used for object prediction consists of two classes, that is knives and pistols. Once any weapon or suspicious activity is detected, an alert message along with the location coordinates as well as a link to live stream the video of the crime scene is sent to the concerned pre-defined contacts. Hence, this helps in crime reconnaissance thus mitigation.